Automatic Counting Research Articles

Automatic Catfish Sorter and Counter Based on Weight Classification

Catfish is one of the main raw materials in the fisheries sector which also supports the Indonesian economy. However, manual sorting of catfish makes the weight of catfish not uniform when marketed. Therefore, tools and machines are needed that automatically sort catfish in large quantities, making it more effective and easier for catfish breeders to use. In this study the process of making the system was realized in 3 stages, namely needs analysis, implementation, and testing of tools. The purpose of this system is to be able to classify the weight of catfish in the large category with fish weighing > 140 grams, medium with fish weighing 80-140 grams and small with fish weighing 20-80 grams. From the system test results, the average accuracy is 99.56. % and a precision of 98.75% for load cell sensor readings, while for E18 infrared sensor readings the results are always known, but data is not always sent and displayed on the LCD screen. Classification of fish categories carried out by the system has worked well, where the system is able to sort and count correctly 14 times out of 15 tests with an accuracy of 93%. The average computing time required for this tool is 3.67 seconds for 15 tests.

AUTOMATIC VEHICLE COUNTER SYSTEM BASED BLOB DETECTION FOR HIGHWAY SURVEILLANCE

The number of vehicles that increase every year has a major impact on the occurrence of congestion and accidents and causes a significant increase in the volume of vehicles, especially on the highway. With this increase, many officers find it difficult to be able to anticipate or supervise vehicles directly. The research that we made, entitled Automatic Vehicle Counter System Based on Blob Detection for Highway Surveillance Using OpenCV, is a solution to this problem because by utilizing image transformation it makes it easier for the system to be able to detect vehicles and identify the number of vehicles entering the lane. The results obtained show an accuracy value of 97.11% based on testing with 10 video samples, with a total of 1329 vehicles detected out of a total of 1362, meaning that the total error is only 3.02%.

Сигнальная алгоритмистика программно-конфигурируемого модуля радиомониторинга с частотно-сканирующей разверткой

The paper presents an analysis of scientific and practical development of software-configurable radio monitoring module based on RTL2832U tuner in Simulink environment in order to scan a given ultra-short-wave (VHF) frequency range and perform automatic search, counting and output of the number of broadcasting stations. Given is a structural and circuit diagram of the modernized SDR-receiver (Software Defined Radio) with the inclusion of a balanced mixer, expanding the frequency range of the SDR-receiver from 1.8 GHz to 4.6 GHz. We developed an algorithm for controlling the software architecture of the scanning radio monitoring module, which searches for radio signals in a given band, calculates the spectral power of radio signals, counts the number of detected radio channels.

Transferable supervised learning model for public transport service load estimation

AbstractDetailed knowledge of service utilisation and passenger load profiles is the basis for the design, operation, and adjustment of a public transport service. The advancement in sensing technologies enable transit operators to monitor the variabilities in passenger flows continuously and consistently. There is a growing body of literature on using supervised learning models with direct passenger counts from historical observations. However, the incomplete, inaccurate, and biased data from automatic sensors pose challenges in this process. This paper proposes novel supervised learning models to estimate the onboard load profile of public transport services based on two main data sources: (1) limited data collected on a subset of service vehicles by automatic passenger counting (APC) systems, and (2) fare data collected by automatic fare collection (AFC) systems. The specific consideration is given to the fact that the developed models can be transferred across different routes. This is motivated by the commonly “limited coverage” of automated passenger counter devices on service vehicles. We introduce an array of new models, including a superior segment-based model, which demonstrates remarkable improvement in model transferability and accuracy. The proposed methodology utilises separate methods in different segments of a transit line. The proposed models were applied to three tram lines in Melbourne, Australia, where various types of shortcomings exist in the automated data. The test results demonstrate that the proposed models can be transferred and applied to other transit route without relying on historical observations. This would enable transit operators to reduce the number of required devices and monitor service utilisation in a more cost-efficiently manner, particularly in public transport networks where AFC coverage is usually incomplete and negatively skewed. The information on service utilisation will not only help operators to accommodate the variability in passenger demand but also assist passengers in journey planning to avoid overcrowding on services.

Tassel-YOLO: A New High-Precision and Real-Time Method for Maize Tassel Detection and Counting Based on UAV Aerial Images

Tassel is an important part of the maize plant. The automatic detection and counting of tassels using unmanned aerial vehicle (UAV) imagery can promote the development of intelligent maize planting. However, the actual maize field situation is complex, and the speed and accuracy of the existing algorithms are difficult to meet the needs of real-time detection. To solve this problem, this study constructed a large high-quality maize tassel dataset, which contains information from more than 40,000 tassel images at the tasseling stage. Using YOLOv7 as the original model, a Tassel-YOLO model for the task of maize tassel detection is proposed. Our model adds a global attention mechanism, adopts GSConv convolution and a VoVGSCSP module in the neck part, and improves the loss function to a SIoU loss function. For the tassel detection task, the mAP@0.5 of Tassel-YOLO reaches 96.14%, with an average prediction time of 13.5 ms. Compared with YOLOv7, the model parameters and computation cost are reduced by 4.11 M and 11.4 G, respectively. The counting accuracy has been improved to 97.55%. Experimental results show that the overall performance of Tassel-YOLO is better than other mainstream object detection algorithms. Therefore, Tassel-YOLO represents an effective exploration of the YOLO network architecture, as it satisfactorily meets the requirements of real-time detection and presents a novel solution for maize tassel detection based on UAV aerial images.

YOLO-C: An Efficient and Robust Detection Algorithm for Mature Long Staple Cotton Targets with High-Resolution RGB Images

Under complex field conditions, robust and efficient boll detection at maturity is an important tool for pre-harvest strategy and yield prediction. To achieve automatic detection and counting of long-staple cotton in a natural environment, this paper proposes an improved algorithm incorporating deformable convolution and attention mechanism, called YOLO-C, based on YOLOv7: (1) To capture more detailed and localized features in the image, part of the 3 × 3 convolution in the ELAN layer of the backbone is replaced by deformable convolution to improve the expressiveness and accuracy of the model. (2) To suppress irrelevant information, three SENet modules are introduced after the backbone to improve the ability of feature maps to express information, and CBAM and CA are introduced for comparison experiments. (3) A WIoU loss function based on a dynamic non-monotonic focusing mechanism is established to reduce the harmful gradients generated by low-quality examples on the original loss function and improve the model performance. During the model evaluation, the model is compared with other YOLO series and mainstream detection algorithms, and the model mAP@0.5 achieves 97.19%, which is 1.6% better than the YOLOv7 algorithm. In the model testing session, the root mean square error and coefficient of determination (R2) of YOLO-C are 1.88 and 0.96, respectively, indicating that YOLO-C has higher robustness and reliability for boll target detection in complex environments and can provide an effective method for yield prediction of long-staple cotton at maturity.

Hematological Normality, Serum Biochemistry, and Acute Phase Proteins in Healthy Beef Calves in the Brazilian Savannah.

The Brazilian savannah region, characterized by high average temperatures, well-defined rainy and dry seasons, soil with low productive potential, and high pressure for parasitic diseases, is home to the highest percentage of the beef herd, which is the world's largest commercial beef producer. Therefore, breeds that present rusticity combined with productivity are the focus of research in cattle breeding in the region. Considering their geographic particularities and their effects on the animals' blood parameters, the objective was to study the behavior of hematological variables, serum biochemistry, and acute phase proteins in beef calves at weaning age. Jugular blood samples were collected in a single day from 30 weaning calves (about eight months old and 200 kg of body weight) from the following breeds: Nellore, Senepol, Nellore × Aberdeen Angus cross, Nellore × Senepol cross, and Nellore × Aberdeen Angus × Senepol cross. Hematological data were obtained using an automatic cell counter, serum biochemical measurements were obtained using commercial kits, and the electrophoretogram was obtained using the SDS-page technique. In general, the results were consistent with data already published in similar situations regarding health status, age, and level of metabolic activity. However, differences observed between groups can be explained by differences observed in other concurrent variables like temperament. The pure zebu breed was more reactive than pure taurine. While crossbreds showed intermediate values, and parasitic infestation, the pure taurine breed with higher parasite infestation, while the zebu breed had lower values, which produced effects in some laboratory tests, and generated differences between breeds. In conclusion, the reference intervals available for healthy animals can be routinely used without interference from the geographic region for animals produced without nutritional failures as long as changes are recorded in pathological, infectious, metabolic, or nutritional deficiency situations. However, it is suggested that a study covering a larger number of herds may demonstrate a greater geographic effect on the studied variables.

Estimation of flow trajectories in a multi-lines transportation network

Characterizing a public transportation network, such as an urban network with multiple lines, requires the origin–destination trip counts during a given period. Yet, if automatic counting makes the embarkment (boarding) and disembarkment (alighting) counts in vehicles known, it often happens that pedestrian transfers between lines are harder to track, and require costly and invasive devices (e.g., facial recognition system) to be estimated. In this contribution, we propose a method, based on maximum entropy and involving an iterative fitting procedure, which estimates the passenger flow between origins and destinations solely based on embarkment and disembarkment counts. Moreover, this method is flexible enough to provide an adaptable framework in case additional data is known, such as attraction poles between certain nodes in the network, or percentages of transferring passengers between some lines. This method is tested on toy examples, as well as with the data of the public transportation network of the city of Lausanne provided by its Transportation Agency (tl), and gives arguably convincing estimations of the transportation flow.

ROI-BMC-DNNet: An efficient automatic analysis model of whole-slide scanned bone marrow aspirate images for the diagnosis of hematological disorders

Cell differential counting in the scanned whole-slide image (WSI) is essential but still challenging in the intelligent diagnosis of hematological disorders based on bone marrow aspirate smear images. The huge size and the complex content of WSI pose multiple obstacles to training an efficient and accurate analysis model. This paper proposes a novel analysis framework termed ROI-BMC-DNNet. To capture patch images in the cell well-spread areas, we provide a pyramid segmentation network based on prior pathological knowledge to automatically locate regions of interest (ROI) that pathologists usually manually focus on to count nucleated cells. Specifically, we describe a simple but effective alignment method for multiscale ROI, aiming to maintain the accuracy of the semantic and the spatial information of the pyramid network. Furthermore, we design a patch sampling algorithm and a patch quality evaluating network to ensure the effectiveness of the sampled patches. Additionally, we provide a cell detection model to realize the automatic differential counting of the nucleated cells in the sampled patches. Experiments are conducted on the datasets of scanned WSIs from 120 patients. Our method achieves 92.27% retrieval precision for the high-quality patches (patches with nucleated cells with better morphology and without particles). When the missed nucleated cells are evaluated, the detection Recall and Precision of the bone marrow nucleated cells from the WSIs are 87.13% and 87.90% respectively. Notably, the recognition Recall and Precision of the blast cell are 91.37% and 90.47%, respectively. The results confirm that the proposed method is promising to work as an auxiliary diagnostic tool for hematological disorders.

NDMFCS: An automatic fruit counting system in modern apple orchard using abatement of abnormal fruit detection

Automatic fruit counting is an important task for growers to estimate yield and manage orchards. Although many deep-learning-based fruit detection algorithms have been developed to improve performance of automatic fruit counting systems, abnormal fruit detection has often been caused by these algorithms detecting non-target fruits that have similar growth characteristics to target fruits. For abnormal fruit detection, detected fruits in the back row of the tree were defined as DFBRT, while detected fruits on the ground were defined as DFG. Both of them would result in a higher number of fruits counting than the ground truth. This study proposes an automatic fruit counting system called NDMFCS (Normal Detection Matched Fruit Counting System) to solve this problem for improving fruit counting accuracy in modern apple orchard. NDMFCS consists of three sub-systems, i.e. object detection based on You Only Look Once Version 4-tiny (YOLOv4-tiny), abatement of abnormal fruit detection based on threshold, and fruit counting based on trunk tracking and identity document (ID) assignment. YOLOv4-tiny was selected to implement detection of fruits and trunks, whose output is confidence and pixel coordinates of detected object. The DFBRT and DFG were abated by thresholds to improve detection performance of fruit. This meant that detected fruits were removed when their distance from camera is further than a distance threshold or the confidence of fruit detection is less than a confidence threshold. Finally, fruit counting was implemented by trunk tracking and ID assignment, where each fruit was assigned a unique tracking ID. Results on 10 sets of original videos indicated that average fruit detection precision was improved from 89.1% to 93.3% after abatement of abnormal fruit detection. Also, Multiple Object Tracking Accuracy and Multiple Object Tracking Precision were improved on average by 4.2% and 3.3%, respectively, while average ID Switch Rate was decreased on average by 1.1%. And average fruit counting accuracy was improved to 95.0% by 4.2%. Coefficient of determination (R2) was 0.97, which indicated the number of fruits counted by NDMFCS was near to the ground truth. These results demonstrate that the abatement of abnormal fruit detection can improve performance of apple counting, which has the potential to provide a technical support for estimating fruit yield in modern apple orchards.

Evaluation of an Automated System for the Counting of Microbial Colonies.

Counting of microbial colonies is a common technique employed in research and diagnostics. To simplify this tedious and time-consuming process, automated systems have been proposed. This study aimed to elucidate the reliability of automated colony counting. We evaluated a commercially available instrument (UVP ColonyDoc-It Imaging Station) in regard to its accuracy and potential time savings. Suspensions of Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, Enterococcus faecium, and Candida albicans (n = 20 each) were adjusted to achieve growth of approximately 1,000, 100, 10, and 1 colony per plate, respectively, after overnight incubation on different solid media. Compared with manual counting, each plate was automatically counted by the UVP ColonyDoc-It with and without visual adjustment on a computer display. For all bacterial species and concentrations automatically counted without visual correction, an overall mean difference from manual counts of 59.7%, a proportion of isolates with overestimation/underestimation of colony numbers of 29%/45%, respectively, and only a moderate relationship (R2 = 0.77) with the manual counting were shown. Applying visual correction, the overall mean difference from manual counts was 1.8%, the proportion of isolates with overestimation/underestimation of colony numbers amounted to 2%/42%, respectively, and a strong relationship (R2 = 0.99) with the manual counting was observed. The mean time needed for manual counting compared with automated counting without and with visual correction was 70 s, 30 s, and 104 s, respectively, for bacterial colonies through all concentrations tested. Generally, similar performance regarding accuracy and counting time was observed with C. albicans. In conclusion, fully automatic counting showed low accuracy, especially for plates with very high or very low colony numbers. After visual correction of the automatically generated results, the concordance with manual counts was high; however, there was no advantage in reading time. IMPORTANCE Colony counting is a widely utilized technique in the field of microbiology. The accuracy and convenience of automated colony counters are essential for research and diagnostics. However, there is only sparse evidence on performance and usefulness of such instruments. This study examined the current state of reliability and practicality of the automated colony counting with an advanced modern system. For this, we thoroughly evaluated a commercially available instrument in terms of its accuracy and counting time required. Our findings indicate that fully automatic counting resulted in low accuracy, particularly for plates with very high or very low colony numbers. Visual correction of the automated results on a computer screen improved concordance with manual counts, but there was no benefit in counting time.

Clinical value of serum soluble receptor for advanced glycation end products in evaluating the condition of patients with bronchial asthma.

Objective: This study focused on the clinical value of serum soluble receptor for advanced glycation end products (sRAGE) levels in evaluating the severity of bronchial asthma (BA). Methods: Serum sRAGE expression was measured by using enzyme-linked immunosorbent assay, eosinophils (EOS) count was measured by using an automatic blood cell counter, and forced expiratory volume in 1 second (FEV1) was measured by pulmonary function analyzer in 120 patients with BA, 40 patients with non-BA pulmonary disease, and 40 healthy controls. Receiver operating characteristic curves were used to analyze the clinical value of sRAGE expression levels, EOS counts, and FEV1 level to assess the severity of illness in the patients with BA. Results: Compared with the healthy controls and the patients without BA, the patients with BA had the lowest serum sRAGE expression level (47.36 ± 6.3 ng/L versus 75.3 ± 6.3 ng/L versus 67.5 ± 5.06 ng/L; p < 0.05), the highest EOS count (231.2 ± 18.3 106/L versus 175.9 ± 15.6 106/L versus 197.8 ± 19.6 106/L; p < 0.05), and the lowest FEV1 level (1.19 ± 0.15 L versus 1.57 ± 0.2 L versus 1.3 ± 0.17 L; p < 0.05). Correlation analysis revealed that the serum sRAGE expression levels were notably negatively correlated with the EOS counts (r value of -0.471, p < 0.05) but significantly positively linked to FEV1 levels (r value of 0.362, p < 0.05). Serum sRAGE expression levels could help in accurately diagnosing patients with severe BA (area under the receiver operating characteristic curve (AUC) = 0.904), whereas prediction in the patients with mild BA was achieved by EOS counts (AUC = 0.857). Conclusion: The serum sRAGE level has potential value in diagnosing the severity of BA, which is conducive to identifying patients with severe BA and guiding in development of new therapeutic strategies.

Determining Free-Flow Speed on Different Classes of Rural Two-Lane Highways

Current analytical free-flow speed models consider all rural two-lane highways as the same road type despite their different functional significance in the network. The aim of this paper is to develop a prediction model for free-flow speed as a function of speed limit and road geometric characteristics for different classes of rural two-lane highways. The research was conducted on 50 representative sections of the two rural classes of two-lane highways equipped with automatic traffic counters in Serbia. In order to develop the appropriate models, it was necessary to determine the threshold values of vehicle time headway in the free-flow for both classes of rural two-lane highways, based on the total number of 191,720 vehicles. The obtained results show that there are differences in the threshold values of free-flow time headway for different road classes. Namely, it was determined that the values of free-flow time headway for Class I amounted to 6.3 s, and for Class II to 8.4 s. The free-flow speed prediction model for different road classes showed that speed limit had the highest impact on free-flow speed for Class I and II highways, followed by horizontal curve radius and shoulder width.

A Real-Time Object Counting and Collecting Device for Industrial Automation Process Using Machine Vision

An efficient product packing system through a simplified automatic counting and collecting process is a critical aspect of the manufacturing industry. In the present paper, we propose a real-time tool for simultaneous counting and collection of the objects into different bins using machine vision. The system employs a minimum distance classifier for object detection, Counting on the conveyor belt is done by tracking the Euclidian distances between the centroids of the objects in successive frames. Furthermore, the required number of object collection is carried out in two steps. In the first step, multiple objects nearest to the required count are collected. In the second step, the difference of objects that remained from the previous step are added one by one. Object collection is carried out by integrating software output to the hardware, which collects them in required numbers in the different bins. To meet the fast counting requirements, a high-speed conveyor system constituting vision sensor with pixel resolution of 1920x1200 and 41 frames per second frame rate is employed. The high computational power requirements for implementing the algorithm is accomplished by employing a dedicated graphics microcontroller, whose GPIO pins are used for interfacing the hardware for the object collection. The testing results of the proposed device with 30 variants of different plumbing industry objects confirm an accuracy of 100% for a period of 3 hours. Moreover, the proposed module is scalable and could boost the productivity of the company in a complex environment without affecting the accuracy of the system.

Deep learning for few-shot white blood cell image classification and feature learning

ABSTRACT Differential counting of white blood cells (WBCs) in bone marrow using artificial intelligence (AI)-based models, such as convolutional neural network (CNN) and its various variants, can help physicians to efficiently diagnose many critical diseases such as leukaemia, AIDS and cancers. In this work, we implement a deep transfer learning to several CNN models to examine their effectiveness on automatically classifying WBCs into lymphocytes and non-lymphocytes groups. Our results show that transfer learning can enhance the training of the model and improve the model performance. We also discover that using image masking to remove irrelevant image pixels can further increase the accuracy of the model predictions. Moreover, we assess the impact of three data augmentation techniques to address the imbalance in the data set, which commonly occurs in many biological applications. Our results show that all the three examined data augmentation methods improve the classification results on both training and testing data sets. Altogether, we demonstrate that deep neural networks, when combined with transfer learning and imaging processing techniques, can serve as a powerful tool to conduct automatic differential counting of WBCs, and thus facilitate the diagnosis of the WBC-related disorders, monitor the disease progression and improve the effectiveness of therapeutics.

Can Dialysis Patients Identify and Diagnose Pulmonary Congestion Using Self-Lung Ultrasound?

With the recent developments in automated tools, smaller and cheaper machines for lung ultrasound (LUS) are leading us toward the potential to conduct POCUS tele-guidance for the early detection of pulmonary congestion. This study aims to evaluate the feasibility and accuracy of a self-lung ultrasound study conducted by hemodialysis (HD) patients to detect pulmonary congestion, with and without artificial intelligence (AI)-based automatic tools. This prospective pilot study was conducted between November 2020 and September 2021. Nineteen chronic HD patients were enrolled in the Soroka University Medical Center (SUMC) Dialysis Clinic. First, we examined the patient's ability to obtain a self-lung US. Then, we used interrater reliability (IRR) to compare the self-detection results reported by the patients to the observation of POCUS experts and an ultrasound (US) machine with an AI-based automatic B-line counting tool. All the videos were reviewed by a specialist blinded to the performer. We examined their agreement degree using the weighted Cohen's kappa (Kw) index. A total of 19 patients were included in our analysis. We found moderate to substantial agreement between the POCUS expert review and the automatic counting both when the patient performed the LUS (Kw = 0.49 [95% CI: 0.05-0.93]) and when the researcher performed it (Kw = 0.67 [95% CI: 0.67-0.67]). Patients were able to place the probe in the correct position and present a lung image well even weeks from the teaching session, but did not show good abilities in correctly saving or counting B-lines compared to an expert or an automatic counting tool. Our results suggest that LUS self-monitoring for pulmonary congestion can be a reliable option if the patient's count is combined with an AI application for the B-line count. This study provides insight into the possibility of utilizing home US devices to detect pulmonary congestion, enabling patients to have a more active role in their health care.

Cover Picture: A visualized automatic particle counting strategy for single‐cell level telomerase activity quantification (View 3/2023)

Cover Picture: A visualized automatic particle counting strategy for single‐cell level telomerase activity quantification (View 3/2023)

WED-344 - Validation of automatic cell counting based on impendaciometry and flow cytometry in ascitic fluid

WED-344 - Validation of automatic cell counting based on impendaciometry and flow cytometry in ascitic fluid

Comparative analysis of total protein, casein, lactose, and fat content in milk of cows suffering from subclinical and clinical mastitis caused by Streptococcus spp.

The aim of the study was to analyse the total protein (TP), casein (CAS), lactose (LAC), and fat content of milk from cows with subclinical (SCM) and clinical mastitis (CM) caused by Streptococcus spp. A total of 60 milk samples from diseased cows and 30 milk samples from healthy cows were included in the study. Milk samples were taken from Holstein-Friesian cows from four dairy farms in Lublin Province. The bacteriological examination of the milk was performed and the somatic cells count in 1 mL of milk was determined using a SomaCount FC automatic cell counter. Determination of TP, CAS, LAC, FAT and FA levels in milk was carried out using a DairySpec FT automated Fourier transform infrared spectrometer. Total protein in milk from HE was significantly higher than in milk from cows with mastitis (4.04% vs 3.57% in milk from SCM cows and 3.7% in milk from CM cows, P = 0.001). The CAS level was 2.73% in milk from CM cows and 2.92% in milk from SCM cows vs 3.30% in milk from HE cows, P = 0.001. The changes in CAS and TP in milk resulted in a significant difference in the CAS/TP ratio (81.7% in milk from HE cows vs 73.8% in milk from CM cows). A decrease in levels was also recorded for LAC (4.8% in milk from HE cows vs 4.51% in milk from SCM cows and 4.01% in milk from CM cows, P = 0.001). The fat level was significantly higher in milk from healthy cows than in milk from cows with mastitis (4.0% vs 2.3% in milk from SCM cows and 1.64% in milk from CM cows, P = 0.001). It should be emphasised that the decrease in the levels of TP, LAC and FAT was significant not only in milk from CM cows but also in milk from SCM cows. This is very unfavourable, because the reduction in the main milk components results in poor quality dairy products and impairs line processes.

Designing an Efficient Infusion Device Management Using .Net Framework (IQ Enterprise Gateway)

Abstract: There are currently no remote monitoring or control options available for intravenous (IV) fluid administration devices. An intelligent infusion pump system for autonomous and remote drug management and monitoring is presented in this study. The flow rate (measured in drops per minute) and infusion-interruption issues were remotely monitored using wireless data transmission to users using computer-based apps. The results of this investigation revealed no distinction between the manual and automatic counting readings. Additionally, the technology can alert users about blocked lines and empty bottles. The study's findings suggest that the prototype might be improved (in terms of features and design) and tested for efficacy and consistency in actual clinical settings.