Automatic Counting Research Articles

The Relationship between Raised Mean Platelet Volume and Severity of Newly Diagnosed Acute Ischaemic Stroke

Introduction and Objective: Stroke is one of the leading causes of mortality and long-term disability throughout the world. Mean platelet volume (MPV), a marker of platelet activation, is calculated and provided by automatic blood cell count equipment during routine complete blood count analysis is a surrogate marker of platelet function and a potential mediator of the association between inflammation and thrombosis. So, to investigate MPV in ischaemic stroke and its role to discriminating more severe stroke from mild one the current study was done. Methods: This observational case control study was carried out in Department of Medicine, Dhaka Medical College Hospital (DMCH) from July 2015 to June 2016. 100 patients with attack of ischaemic stroke who presented within 24 hours of symptoms onset were divided into 2 groups (group-1: mRS score 0-2, and group-2: mRS score 3 or more) based on severity. Severity of ischaemic stroke was assessed by the modified Rankin Scale. At the same time 100 age and sex matched controls were taken. Blood samples were collected from both groups and MPV was measured. The results were statistically compared by SPSS 22.0. Statistical significance was accepted at <0.05. Results: The MPV values were more in case group in comparison to control group (10.88±1.23 vs 10.03±1.03; z score-5.31; p value <0.01). Comparison between the 2 groups revealed that MPV value was higher and more significant in group 2 of cases than group 1(10.15± 0.739 in group 1 versus 11.63± 1.04 in group having 2; z score-7.7, p value <0.001). 6 patients of group 2, who could not overcome the situation and died within 24 hours of admission had MPV value of 12.92±0.30. After controlling the risk profiles associated with ischaemic stroke by means of binary logistic regression model, the effect of MPV in ischaemic stroke remained statistically significant (OR: 4.418, 95% CI 2.468-7.908, p= 0.000). ROC curve was 0.85 and standard error 0.039 (95% CI: 0.779-0.932; p<0.01), ...

Automatic Apple Detection and Counting with AD-YOLO and MR-SORT.

In the production management of agriculture, accurate fruit counting plays a vital role in the orchard yield estimation and appropriate production decisions. Although recent tracking-by-detection algorithms have emerged as a promising fruit-counting method, they still cannot completely avoid fruit occlusion and light variations in complex orchard environments, and it is difficult to realize automatic and accurate apple counting. In this paper, a video-based multiple-object tracking method, MR-SORT (Multiple Rematching SORT), is proposed based on the improved YOLOv8 and BoT-SORT. First, we propose the AD-YOLO model, which aims to reduce the number of incorrect detections during object tracking. In the YOLOv8s backbone network, an Omni-dimensional Dynamic Convolution (ODConv) module is used to extract local feature information and enhance the model's ability better; a Global Attention Mechanism (GAM) is introduced to improve the detection ability of a foreground object (apple) in the whole image; a Soft Spatial Pyramid Pooling Layer (SSPPL) is designed to reduce the feature information dispersion and increase the sensory field of the network. Then, the improved BoT-SORT algorithm is proposed by fusing the verification mechanism, SURF feature descriptors, and the Vector of Local Aggregate Descriptors (VLAD) algorithm, which can match apples more accurately in adjacent video frames and reduce the probability of ID switching in the tracking process. The results show that the mAP metrics of the proposed AD-YOLO model are 3.1% higher than those of the YOLOv8 model, reaching 96.4%. The improved tracking algorithm has 297 fewer ID switches, which is 35.6% less than the original algorithm. The multiple-object tracking accuracy of the improved algorithm reached 85.6%, and the average counting error was reduced to 0.07. The coefficient of determination R2 between the ground truth and the predicted value reached 0.98. The above metrics show that our method can give more accurate counting results for apples and even other types of fruit.

Evaluating deep learning techniques for optimal neurons counting and characterization in complex neuronal cultures.

The counting and characterization of neurons in primary cultures have long been areas of significant scientific interest due to their multifaceted applications, ranging from neuronal viability assessment to the study of neuronal development. Traditional methods, often relying on fluorescence or colorimetric staining and manual segmentation, are time consuming, labor intensive, and prone to error, raising the need for the development of automated and reliable methods. This paper delves into the evaluation of three pivotal deep learning techniques: semantic segmentation, which allows for pixel-level classification and is solely suited for characterization; object detection, which focuses on counting and locating neurons; and instance segmentation, which amalgamates the features of the other two but employing more intricate structures. The goal of this research is to discern what technique or combination of those techniques yields the optimal results for automatic counting and characterization of neurons in images of neuronal cultures. Following rigorous experimentation, we conclude that instance segmentation stands out, providing superior outcomes for both challenges.

A Coffee Plant Counting Method Based on Dual-Channel NMS and YOLOv9 Leveraging UAV Multispectral Imaging

Accurate coffee plant counting is a crucial metric for yield estimation and a key component of precision agriculture. While multispectral UAV technology provides more accurate crop growth data, the varying spectral characteristics of coffee plants across different phenological stages complicate automatic plant counting. This study compared the performance of mainstream YOLO models for coffee detection and segmentation, identifying YOLOv9 as the best-performing model, with it achieving high precision in both detection (P = 89.3%, mAP50 = 94.6%) and segmentation performance (P = 88.9%, mAP50 = 94.8%). Furthermore, we studied various spectral combinations from UAV data and found that RGB was most effective during the flowering stage, while RGN (Red, Green, Near-infrared) was more suitable for non-flowering periods. Based on these findings, we proposed an innovative dual-channel non-maximum suppression method (dual-channel NMS), which merges YOLOv9 detection results from both RGB and RGN data, leveraging the strengths of each spectral combination to enhance detection accuracy and achieving a final counting accuracy of 98.4%. This study highlights the importance of integrating UAV multispectral technology with deep learning for coffee detection and offers new insights for the implementation of precision agriculture.

Unsupervised Learning for the Automatic Counting of Grains in Nanocrystals and Image Segmentation at the Atomic Resolution.

Identifying the grain distribution and grain boundaries of nanoparticles is important for predicting their properties. Experimental methods for identifying the crystallographic distribution, such as precession electron diffraction, are limited by their probe size. In this study, we developed an unsupervised learning method by applying a Gabor filter to HAADF-STEM images at the atomic level for image segmentation and automatic counting of grains in polycrystalline nanoparticles. The methodology comprises a Gabor filter for feature extraction, non-negative matrix factorization for dimension reduction, and K-means clustering. We set the threshold distance and angle between the clusters required for the number of clusters to converge so as to automatically determine the optimal number of grains. This approach can shed new light on the nature of polycrystalline nanoparticles and their structure-property relationships.

YOLOv7-KDT: An ensemble model for pomelo counting in complex environment

Pomelo counting recognition in complex environments faces many difficulties, mainly including changes in illumination, occlusion, and complex backgrounds. These factors contribute to the poor recognition performance of early models such as Faster R-CNN, SSD, and Efficientdet. This study optimized the YOLOv7 object detection network through several strategies for this issue. The main optimization strategies are: (1) Introducing the SEAM module to improve the detection ability for occluded objects; (2) Using the AIFI attention mechanism to enhance the network’s recognition of small objects; (3) Adopting the LAMP pruning technique to reduce model complexity; (4) Utilizing multi-teacher knowledge distillation for model compression and performance enhancement. The results show that under the assisted learning of the student module by the multi-teacher module distillation, the proposed method improves mAP by more than 1.9% compared to the aforementioned early models and other networks. After LAMP pruning, the model size is reduced by at least 93.6%. After knowledge distillation processing, its mAP is increased from 84.2% to 91.1%, achieving high-precision pomelo counting recognition. This study effectively solves automatic pomelo recognition and counting in complex environments. The method can be generalized to other agricultural product detection and statistics scenarios, having broad application prospects.

An easier life to come for mosquito researchers: field-testing across Italy supports VECTRACK system for automatic counting, identification and absolute density estimation of Aedes albopictus and Culex pipiens adults

BackgroundDisease-vector mosquito monitoring is an essential prerequisite to optimize control interventions and evidence-based risk predictions. However, conventional entomological monitoring methods are labor- and time-consuming and do not allow high temporal/spatial resolution. In 2022, a novel system coupling an optical sensor with machine learning technologies (VECTRACK) proved effective in counting and identifying Aedes albopictus and Culex pipiens adult females and males. Here, we carried out the first extensive field evaluation of the VECTRACK system to assess: (i) whether the catching capacity of a commercial BG-Mosquitaire trap (BGM) for adult mosquito equipped with VECTRACK (BGM + VECT) was affected by the sensor; (ii) the accuracy of the VECTRACK algorithm in correctly classifying the target mosquito species genus and sex; (iii) Ae. albopictus capture rate of BGM with or without VECTRACK.MethodsThe same experimental design was implemented in four areas in northern (Bergamo and Padua districts), central (Rome) and southern (Procida Island, Naples) Italy. In each area, three types of traps—one BGM, one BGM + VECT and the combination of four sticky traps (STs)—were rotated each 48 h in three different sites. Each sampling scheme was replicated three times/area. Collected mosquitoes were counted and identified by both the VECTRACK algorithm and operator-mediated morphological examination. The performance of the VECTRACK system was assessed by generalized linear mixed and linear regression models. Aedes albopictus capture rates of BGMs were calculated based on the known capture rate of ST.ResultsA total of 3829 mosquitoes (90.2% Ae. albopictus) were captured in 18 collection-days/trap/site. BGM and BGM + VECT showed a similar performance in collecting target mosquitoes. Results show high correlation between visual and automatic identification methods (Spearman Ae. albopictus: females = 0.97; males = 0.89; P < 0.0001) and low count errors. Moreover, the results allowed quantifying the heterogeneous effectiveness associated with different trap types in collecting Ae. albopictus and predicting estimates of its absolute density.ConclusionsObtained results strongly support the VECTRACK system as a powerful tool for mosquito monitoring and research, and its applicability over a range of ecological conditions, accounting for its high potential for continuous monitoring with minimal human effort.Graphical

Automated counting and classifying Daphnia magna using machine vision

Daphnia magna (D. magna) is a model organism widely used in aquatic ecotoxicology research due to its sensitivity to environmental changes. The survival and reproduction rates of D. magna are easily affected by toxic environments. However, their small size, fragility, and transparency, especially in neonate stages, make them challenging to count accurately. Traditionally, counting adult and neonate D. magna relies on manual separation and visual observation, which is not only tedious but also prone to inaccuracies. Previous attempts to aid counting with optical sensors have faced issues such as inducing stress damage due to vertical movement and an inability to distinguish between adults and neonates. With the advancement of deep learning technologies, our study employs a simple light source culture device and utilizes the Mask2Former model to analyze D. magna against the background. Additionally, the U-Net model is used for comparative analysis. We also applied OpenCV technology for automatic counting of adult and neonate D. magna. The model's results were compared against manual counting performed by experienced technicians. Our approach achieves an average relative accuracy of 99.72 % for adult D. magna and 98.30 % for neonate. This method not only enhances counting accuracy but also provides a fast and reliable technique for studying the survival and reproduction rates of D. magna as a model organism.

Video-based automatic people counting for public transport: On-bus versus off-bus deployment

Interest in Automatic People Counting (APC) for crowd detection and management is rapidly growing. While a range of Internet of Things (IoT) sensors and systems exist, video analytics is emerging as a particularly attractive option — especially for applications where more traditional methods of people counting are not available, unreliable or expensive. In this paper we focus on automatic people counting in the public transport context – specifically, rail replacement bus services – in which bus companies are typically contracted to provide bus services to replace train services during periods of planned and unplanned line disruption. This presents a particularly compelling use-case for video-based people counting, while also presenting unique challenges. Field trials are thus vital to the proper assessment of video-based APC solutions, however remain relatively scarce in the literature. While datasets to support research and benchmarking exist, these do not capture the intrinsic complexities of real-world deployment and the implications of selected configurations — in particular, on-vehicle versus off-vehicle use cases. In this paper, we evaluate our own video-based APC solution, representative of state-of-the-art approaches in the literature, in two separate extensive (i.e, multi-day) metropolitan field trials covering both on and off-bus use-cases. Through real-world deployment of the system in both settings, we highlight key differences with respect to APC accuracy, as well as other practical considerations, and the validity of underlying assumptions in both on and off-bus scenarios.

Image Recognition for Floating Waste Monitoring in a Traditional Surface Irrigation System

In the traditional surface irrigation system of Vega Baja del Segura (Spain), large amounts of floating waste accumulate at certain points of the river, irrigation channels and drainage ditches, causing malfunctioning of the irrigation network and rising social problems related to the origins of waste. This work proposes a standardized and quick methodology to characterize the floating waste to detect changes in its amount and components. A dataset was created with 477 images of floating plastic items in different environments and was used for training an algorithm based on YOLOv5s. The mean Average Precision of the trained algorithm was 96.9%, and the detection speed was 81.7 ms. Overhead photographs were taken with an unmanned aerial vehicle at strategic points of the river and channels, and its automatic count of floating objects was compared with their manual count. Both methods showed good agreement, confirming that water bottles were the most abundant (95%) type of floating waste. The automatic count reduced the required time and eliminated human bias in image analysis of the floating waste. This procedure can be used to test the reach of corrective measures implemented by local authorities to prevent floating waste in the river.

Simultaneous enumeration of yeast and bacterial cells in the context of industrial bioprocesses

Abstract In scenarios where yeast and bacterial cells coexist, it is of interest to simultaneously quantify the concentrations of both cell types, since traditional methods used to determine these concentrations individually take more time and resources. Here, we compared different methods for quantifying the fuel ethanol Saccharomyces cerevisiae PE-2 yeast strain and cells from the probiotic Lactiplantibacillus plantarum strain in microbial suspensions. Individual suspensions were prepared, mixed in 1:1 or 100:1 yeast-to-bacteria ratios, covering the range typically encountered in sugarcane biorefineries, and analyzed using bright field microscopy, manual and automatic Spread-plate and Drop-plate counting, flow cytometry (at 1:1 and 100:1 ratios), and a Coulter counter (at 1:1 and 100:1 ratios). We observed that for yeast cell counts in the mixture (1:1 and 100:1 ratios), flow cytometry, the Coulter counter, and both Spread-plate options (manual and automatic CFU counting) yielded statistically similar results, while the Drop-plate and microscopy-based methods gave statistically different results. For bacterial cell quantification, the microscopy-based method, Drop-plate, and both Spread-plate plating options and flow cytometry (1:1 ratio) produced no significantly different results (P &amp;gt; 0.05). In contrast, the Coulter counter (1:1 ratio) and flow cytometry (100:1 ratio) presented results statistically different (P &amp;lt; 0.05). Additionally, quantifying bacterial cells in a mixed suspension at a 100:1 ratio wasn't possible due to an overlap between yeast cell debris and bacterial cells. We conclude that each method has limitations, advantages, and disadvantages.

Combining OBIA, CNN, and UAV imagery for automated detection and mapping of individual olive trees

The identification of individual trees is an important research topic in forestry, remote sensing and computer vision. It represents a tool for effectively and efficiently managing and maintaining forests and orchards. However, this task is not as simple as it seems; tree detection and counting can be time consuming, cost-prohibitive and accuracy-limited, especially if performed manually on a large scale.The availability of very high-resolution UAV imagery with remote sensing can make the counting process easier, faster and more precise. With the development of technology, this process can be made more automated by using intelligent algorithms such as CNN.This work presents an OBIA-CNN (Object Based Image Analysis-Convolution Neural Network) approach that combines CNNs with OBIA to automatically detect and count olive trees from Phantom4 advanced drone imagery. Initially, The CNN-based classifier was created, trained, validated, and applied to generate the Olive trees probability maps on the ortho-photo. The post-classification refinement based on OBIA was then conducted. A super-pixel segmentation and the Excess Green index were performed and a detailed accuracy analysis has been carried out to establish the suitability of the proposed method.The application to a RGB ortho-mosaic of an olive grove, in the east region of Morocco was successful using a manually elaborated training dataset of 4500 images of 24×24 pixels. Finally, the CNN detected and counted 2934 olive trees on the ortho-photo, achieving an overall accuracy of 97 % and 99 % after the OBIA refinement. The results of the proposed OBIA-CNN method were also compared with the classification results of using the Template matching technique, CNN method alone, and OBIA analysis alone to evaluate the performance of the approach. Our findings suggest the use of very high resolution images with object-based deep learning is promising for automatic detection and counting of olive trees to support the accurate and sustainable agricultural monitoring.

Advancing primate surveillance with image recognition techniques from unmanned aerial vehicles.

Using unmanned aerial vehicles (UAVs) for surveys on thermostatic animals has gained prominence due to their ability to provide practical and precise dynamic censuses, contributing to developing and refining conservation strategies. However, the practical application of UAVs for animal monitoring necessitates the automation of image interpretation to enhance their effectiveness. Based on our past experiences, we present the Sichuan snub-nosed monkey (Rhinopithecus roxellana) as a case study to illustrate the effective use of thermal cameras mounted on UAVs for monitoring monkey populations in Qinling, a region characterized by magnificent biodiversity. We used the local contrast method for a small infrared target detection algorithm to collect the total population size. Through the experimental group, we determined the average optimal grayscale threshold, while the validation group confirmed that this threshold enables automatic detection and counting of target animals in similar datasets. The precision rate obtained from the experiments ranged from 85.14% to 97.60%. Our findings reveal a negative correlation between the minimum average distance between thermal spots and the count of detected individuals, indicating higher interference in images with closer thermal spots. We propose a formula for adjusting primate population estimates based on detection rates obtained from UAV surveys. Our results demonstrate the practical application of UAV-based thermal imagery and automated detection algorithms for primate monitoring, albeit with consideration of environmental factors and the need for data preprocessing. This study contributes to advancing the application of UAV technology in wildlife monitoring, with implications for conservation management and research.

The Impact of Tree Density on Automated Oil Palm Tree Counting Accuracy

The rapid development of oil palm plantations in Kualuh Leidong Subdistrict, Labuhanbatu Utara District, North Sumatra Province has led to an increased need for effective and efficient monitoring and supervision of oil palm trees. One method that supports such monitoring and supervision is the use of an automatic counting method using orthophoto data. This orthophoto data was used for automatic tree counting using a deep learning method with the Faster R-CNN algorithm. The study considered two planting patterns: regular planting patterns with spacing of 4 to 9 meters, and random planting patterns with varying spacing. Data processing involved an epoch value of 80 and a batch size value of 4. The accuracy of the automatic oil palm tree counting was evaluated based on the density of the spacing between trees with reference to the ground truth. The findings indicated that the deep learning Faster R-CNN algorithm achieved higher accuracy in automatic calculations for regular planting patterns.

Real-time monitoring of middle cerebral artery blood flow using intraoperative transcranial doppler during trans-carotid artery revascularization

ObjectiveTranscarotid artery revascularization (TCAR) has emerged as a safe and effective method for carotid revascularization, offering several key advantages over carotid endarterectomy (CEA) and carotid artery stenting (CAS). Intraoperative transcranial Doppler (TCD) monitoring plays a pivotal role in assessing cerebral hemodynamics and detecting embolic signals during TCAR at our institution. MethodsThis review synthesizes the current literature and provides guidance for TCD monitoring throughout the various phases of TCAR, from preoperative assessment to postoperative management. Key considerations include probe placement, waveform evaluation, interpreting monitoring parameters such as mean flow velocity (MFV), pulsatility index (PI), and percentage change in the MFV (Δ%). Techniques for maintaining the insonation of the middle cerebral artery (MCA) M1 segment and optimal parameter settings for intraoperative TCD monitoring are detailed. ResultsTCAR phases are highlighted, including transcarotid access and vessel control, sheath insertion, the establishment of flow reversal, pre-dilation, stent placement, post-dilation, and closure, while emphasizing the importance of real-time feedback provided by TCD monitoring in identifying embolic signals and assessing changes in cerebral perfusion. The review discusses limitations of TCD monitoring, such as inadequate temporal windows, incorrect vessel identification and reliability issues with automatic emboli detection counters. Furthermore, practical advice is provided on how to navigate common pitfalls encountered during intraoperative TCD monitoring. ConclusionBy understanding the nuances of TCD monitoring and its application in TCAR, intraoperative TCD monitoring may aid to minimize the low but potential risk of intraprocedural embolic events, periprocedural hypoperfusion and postoperative hyperperfusion. Finally, we suggest opportunities for further research in embolization quantification and additional strategies to optimize quality control in TCAR.

Green pepper fruits counting based on improved DeepSort and optimized Yolov5s.

Green pepper yield estimation is crucial for establishing harvest and storage strategies. This paper proposes an automatic counting method for green pepper fruits based on object detection and multi-object tracking algorithm. Green pepper fruits have colors similar to leaves and are often occluded by each other, posing challenges for detection. Based on the YOLOv5s, the CS_YOLOv5s model is specifically designed for green pepper fruit detection. In the CS_YOLOv5s model, a Slim-Nick combined with GSConv structure is utilized in the Neck to reduce model parameters while enhancing detection speed. Additionally, the CBAM attention mechanism is integrated into the Neck to enhance the feature perception of green peppers at various locations and enhance the feature extraction capabilities of the model. According to the test results, the CS_YOLOv5s model of mAP, Precision and Recall, and Detection time of a single image are 98.96%, 95%, 97.3%, and 6.3 ms respectively. Compared to the YOLOv5s model, the Detection time of a single image is reduced by 34.4%, while Recall and mAP values are improved. Additionally, for green pepper fruit tracking, this paper combines appearance matching algorithms and track optimization algorithms from SportsTrack to optimize the DeepSort algorithm. Considering three different scenarios of tracking, the MOTA and MOTP are stable, but the ID switch is reduced by 29.41%. Based on the CS_YOLOv5s model, the counting performance before and after DeepSort optimization is compared. For green pepper counting in videos, the optimized DeepSort algorithm achieves ACP (Average Counting Precision), MAE (Mean Absolute Error), and RMSE (Root Mean Squared Error) values of 95.33%, 3.33, and 3.74, respectively. Compared to the original algorithm, ACP increases by 7.2%, while MAE and RMSE decrease by 6.67 and 6.94, respectively. Additionally, Based on the optimized DeepSort, the fruit counting results using YOLOv5s model and CS_YOLOv5s model were compared, and the results show that using the better object detector CS_YOLOv5s has better counting accuracy and robustness.

Study of some components of the influence and formation of blood flow in patients with "slow flow".

"Slow flow" is one very important concept in modern fundamental and clinical biomedicine. Slow coronary flow is indicative of delayed filling of the terminal coronary artery vessels, occurring in the absence of significant coronary stenosis. This group patient of patients exhibits a high incidence of disability and represents a significant financial and material burden for the state and the healthcare system in general. The primary objective of our study was to examine patients with slow coronary flow. We studied the standard parameters recommended by the international health care system (electrocardiography (by Medica QRS-12, Germany), through the electrical activity of a patient's heart by the electrical impulses (beating) of the heart; HC1(Germany); coagulogramma by Coatron M1 (Germany), troponin by AQT 90 (Germany); general blood test we used automatic human counting device HC1(Germany). Also, we investigate the original parameters (non-standard parameters, which we use in this pilot study) that we were first studied for this diagnosis and non-standard parameters. A general blood test showed that patients with slow flow had a higher blood leukocyte count than the control group, but the amount of hemoglobin was normal, the hematocrit was much higher than in the control group, and the platelet count was close to the lower limit of clinical standards.We obtained details of blood flow by coagulation situation, such as prothrombin time, prothrombin index, international normalized ratio, activated partial thromboplastin time, thrombin time, fibrinogen, and rheological properties such as index of erythrocyte aggregability, index of erythrocyte deformability, plasma viscosity, in silico blood rheological index. Blood flow can be considered as a superposition of vortices with similar frequencies and wave vectors that change after bifurcations or other obstacles in the vascular network. These factors together determine the conditions for structuring the flow of moving blood. Disruption or alteration of these factors results in slow flow. It has been found that the speed of blood flow in the coronary arteries depends on changes in the number and function of red blood cells. Slow flow is directly influenced by the aggregation and deformation of red blood cells, their number, and plasma viscosity. Consequently, the rheological status plays a crucial role in determining blood flow and its velocity.

Robust deep learning based shrimp counting in an industrial farm setting

Shrimp production is one of the fastest growing sectors in the aquaculture industry. Despite extensive research in recent years, stocking densities in shrimp systems still depend on manual sampling which is neither time nor cost efficient and additionally challenges shrimp welfare. This paper compares the performance of automatic shrimp counting solutions for commercial Recirculating Aquaculture System (RAS) based farming systems, using eight Deep Learning based methods. The entire dataset includes 1379 images of shrimps in RAS farming tanks, taken at a distance using an iPhone 11 mini. These were manually annotated, with bounding boxes for every clearly visible shrimp. The dataset was partitioned into training (60 %, 828 samples), validation (20 %, 276 samples) and test (20 %, 275 samples) splits for purposes of training and evaluating the models. The present work demonstrates that state-of-the-art object detection models outperform manual counting and achieve high performance across the entire production range and at various circumstances known to be challenging for object detection (dim light, overlapping and small animals, various acquisition devices and image resolutions and camera distance to object). Highest counting performance was obtained with models based on YOLOv5m6 and Faster R–CNN (as opposed to neural network autoencoder architecture to estimate a density map). The best model generalizes well on an independent test set and even shows promising performance when tested with different taxa. The model performs best at densities below 200 shrimps per image with an overall error of 5.97 %. It is assumed that this performance can be improved by increasing the dataset size, especially with images at high shrimp stocking density, and it is strongly believed that a performance below the 5 % error threshold is close to being achieved, which will allow for deployment of the model in an industrial setting.

Large-scale performance assessment of the BG-Counter 2 used with two different mosquito traps

BackgroundMosquitoes are important vectors of pathogens. They are usually collected with CO2-baited traps and subsequently identified by morphology. This procedure is very time-consuming. Automatic counting traps could facilitate timely evaluation of the local risk for mosquito-borne pathogen transmission or decision-making on vector control measures, but the counting accuracy of such devices has rarely been validated in the field.MethodsThe Biogents (BG)-Counter 2 automatically counts mosquitoes by discriminating the size of captured objects directly in the field and transmits the data to a cloud server. To assess the accuracy of this counting device, 27 traps were placed at 19 sampling sites across Germany and used in daily, weekly or bimonthly intervals from April until October 2021. The BG-Counter 2 was attached to a CO2-trap (BG-Pro trap = CO2-Pro) and the same trap was converted to also attract gravid mosquitoes (upside-down BG-Pro trap with a water container beneath = CO2-Pro-gravid). All captured mosquitoes were identified by morphology. The number of females (unfed and gravid), mosquito diversity and the number of identified specimens in relation to the counting data of the BG-Counter were compared between the two trapping devices to evaluate sampling success and counting accuracy.ResultsIn total 26,714 mosquitoes were collected during 854 trap days. The CO2-Pro-gravid trap captured significantly more mosquitoes per trap day for all specimens, gravid females and non-gravid females, while there was no difference in the mosquito diversity. The linear model with the captured mosquitoes as a response and the counted specimens as a predictor explained only a small degree of the variation within the data (R2 = 0.16), but per individual trap the value could reach up to 0.62 (mean R2 = 0.23). The counting accuracy for the daily samples had a significant positive correlation with sample size, resulting in higher accuracy for the CO2-Pro-gravid trap and higher accuracy for sites and sampling months with high mosquito abundance.ConclusionsWhile the accuracy of the BG-Counter 2 is quite low, the device is able to depict mosquito phenology and provide information about local population dynamics.Graphical

Concurrent Immediate Reference Counting

Memory management for optimistic concurrency in unmanaged programming languages is challenging. Safe memory reclamation (SMR) algorithms help address this, but they are difficult to use correctly. Automatic reference counting provides a simpler interface, but it has been less efficient than SMR algorithms. Recently, there has been a push to apply the optimizations used in garbage collectors for managed languages to elide reference count updates from local references. Notably, Fast Reference Counter, OrcGC, and Concurrent Deferred Reference Counting use SMR algorithms to protect local references by deferring decrements or reclamation. While they show a significant performance improvement, their use of deferral may result in growing memory usage due to slow reclamation of linked structures, and suboptimal performance in update-heavy workloads. We present Concurrent Immediate Reference Counting (CIRC), a new combination of SMR algorithms with reference counting. CIRC employs deferral like other modern methods, but it avoids their problems with novel algorithms for (1) immediately reclaiming linked structures recursively by tracking the reachability of each object, and (2) applying decrements immediately and deferring only the reclamation. Our experiments show that CIRC’s memory usage does not grow over time and is only slightly higher than the underlying SMR. Moreover, CIRC further narrows the performance gap between the underlying SMR, positioning it as a promising solution to safe automatic memory management for highly concurrent data structures in unmanaged languages.