Track Counting Research Articles

Real-Time Tracking System for City Bus Location And Passenger Count

This survey paper reviews the advancements in real-time tracking and passenger counting systems for city buses, emphasizing the growing need for efficient, data-driven public transit solutions. As urban populations rise, city transport systems face challenges in managing demand, reducing congestion, and improving service reliability. Real-time tracking systems using GPS, GIS, and IoT-enabled sensors enable passengers and transit authorities to monitor bus locations and optimize travel times. Additionally, passenger counting systems—utilizing infrared, AI-based vision models, and pressure sensors—provide data on bus occupancy levels, allowing commuters to make informed travel decisions and helping transit agencies manage resources efficiently. This paper examines existing technologies, compares various tracking and counting methods based on accuracy, cost, and scalability, and explores recent trends in artificial intelligence, big data analytics, and smart city integration. Furthermore, it identifies critical challenges, including data privacy, accuracy in urban environments, and energy consumption, proposing future research directions for enhancing the reliability, sustainability, and user-friendliness of these systems. Our findings underscore the transformative potential of real-time bus tracking and passenger counting in advancing public transportation infrastructure and enhancing urban mobility

Apatite Fission-Track Dating: A Comparative Study of Ages Obtained by the Automated Counting LA-ICP-MS and External Detector Methodologies

Abstract Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) in combination with developments in digital microscopy, image analysis, and computer software has allowed the implementation of an automated counting approach for the apatite fission-track (AFT) analysis. We refer to this approach as the “automated counting-LA-ICP-MS” (ACLA) method. Two major components comprise the ACLA method: (i) the digital counting of spontaneous tracks performed on high-resolution images captured from apatite grains and (ii) the measurement of 238U content in apatite by LA-ICP-MS. This study includes ACLA analyses from Fish Canyon Tuff (FCT) and Durango apatite standard crystals. Furthermore, a comparative age study between the ACLA and conventional external detector method (EDM) strategies was performed on a set of thirteen granitoid samples from northwestern Mexico and four granitic samples from the eastern Dharwar craton (EDC), India. ACLA analyses on FCT yielded an AFT age of 28.1 ± 0.6 (1σ) and 28.8 ± 1.1 (1σ) Ma for Durango apatite, whereas reported EDM ages are 27.5 ± 0.5 and 31.4 ± 0.5 Ma, respectively. Calculated AFT ages using the ACLA method from northwestern Mexico samples range from 11.1 ± 1.1 to 42.0 ± 3.6 Ma (EDM ages range from 10.0 ± 0.8 to 54.0 ± 3.0 Ma), whereas AFT ages from the EDC samples range from 147 ± 3.1 to 220.5 ± 12.5 Ma (EDM ages range from 120.9 ± 4.5 to 197.1 ± 19.4 Ma). Based on a statistical comparison with ages previously determined by the conventional EDM on the same samples and considering their 2σ uncertainties, these ages are in good agreement.

The WAVE complex forms linear arrays at negative membrane curvature to instruct lamellipodia formation.

Cells generate a wide range of actin-based membrane protrusions for various cell behaviors. These protrusions are organized by different actin nucleation promoting factors. For example, N-WASP controls finger-like filopodia, whereas the WAVE complex controls sheet-like lamellipodia. These different membrane morphologies likely reflect different patterns of nucleator self-organization. N-WASP phase separation has been successfully studied through biochemical reconstitutions, but how the WAVE complex self-organizes to instruct lamellipodia is unknown. Because WAVE complex self-organization has proven refractory to cell-free studies, we leverage in vivo biochemical approaches to investigate WAVE complex organization within its native cellular context. With single molecule tracking and molecular counting, we show that the WAVE complex forms highly regular multilayered linear arrays at the plasma membrane that are reminiscent of a microtubule-like organization. Similar to the organization of microtubule protofilaments in a curved array, membrane curvature is both necessary and sufficient for formation of these WAVE complex linear arrays, though actin polymerization is not. This dependency on negative membrane curvature could explain both the templating of lamellipodia and their emergent behaviors, including barrier avoidance. Our data uncover the key biophysical properties of mesoscale WAVE complex patterning and highlight an integral relationship between NPF self-organization and cell morphogenesis.

Analysis of Fluorescent Proteins for Observing Single Gene Locus in a Live and Fixed Escherichia coli Cell.

Fluorescent proteins (FPs) are essential tools for advanced microscopy techniques such as super-resolution imaging, single-particle tracking, and quantitative single-molecule counting. Various FPs fused to DNA-binding proteins have been used to observe the subcellular location and movement of specific gene loci in living and fixed bacterial cells. However, quantitative assessments of the properties of FPs for gene locus measurements are still lacking. Here, we assessed various FPs to observe specific gene loci in live and fixed Escherichia coli cells using a fluorescent repressor-operator binding system (FROS), tet operator-Tet repressor proteins (TetR). Tsr-fused FPs were used to assess the intensity and photostability of various FPs (five red FPs: mCherry2, FusionRed, mRFP, mCrimson3, and dKatushka; and seven yellow FPs: SYFP2, Venus, mCitrine, YPet, mClover3, mTopaz, and EYFP) at the single-molecule level in living cells. These FPs were then used for gene locus measurements using FROS. Our results indicate that TetR-mCrimson3 (red) and TetR-EYFP (yellow) had better properties for visualizing gene loci than the other TetR-FPs. Furthermore, fixation procedures affected the clustering of diffusing TetR-FPs and altered the locations of the TetR-FP foci. Fixation with formaldehyde consistently disrupted proper DNA locus observations using TetR-FPs. Notably, the foci measured using TetR-mCrimson3 remained close to their original positions in live cells after glyoxal fixation. This in vivo study provides a cell-imaging guide for the use of FPs for gene-locus observation in E. coli and a scheme for evaluating the use of FPs for other cell-imaging purposes.

Object Detection for Unmanned Aerial Vehicles: A Comprehensive Review

Goal: Researchers studying artificial intelligence have focused a lot of emphasis on computer vision in drones. Drones with intelligence can tackle a lot of issues in real time. For the purpose of monitoring particular surroundings, computer vision tasks like object identification, object tracking, and object counting are important. It becomes increasingly difficult to do, though, due to elements like motion blur, occlusion, camera angle, and altitude. Methodology: A thorough assessment of the literature on object identification and tracking with unmanned aerial vehicles (UAVs) in relation to various applications has been done for this research. This study highlights the research gaps and provides a summary of the results of previous studies. Contribution: Detailed and categorized object identification techniques are used in UAV photos. A selection of UAV datasets tailored to object identification tasks is provided. Summaries of current research projects in various applications are provided. In order to alleviate highlighted research limitations, a secure onboard processing system on a strong object detection framework in precision agriculture is finally presented.

Advanced methods for yield mapping in tart cherries: tank change tracking and YOLO-DeepSort fruit counting

Advanced methods for yield mapping in tart cherries: tank change tracking and YOLO-DeepSort fruit counting

Monitoring wildlife abundance through track surveys: A capture-mark-recapture inspired approach to assess track detection by certified trackers in the Kalahari, Botswana

The surveying of animal tracks is a cost-effective wildlife monitoring technique in conservation, provided the substrate is suitable for tracks to imprint and observers are skilled enough to detect and identify them. However, as with every method quantifying population abundance, it can be biased if track detectability is not accounted for. Track detectability has two components: probability p1ˆ for an animal movement path to intersect with the sampling unit at least once and probability p2ˆ of track detection. Here, we measured p2ˆ, often overlooked, and tested to what extent it is affected by observers and sampling conditions, by applying a capture-mark-recapture approach to a driven track survey of 12 wildlife species in the Kalahari, Botswana. We also characterized and quantified disagreements between pairs of observers on the identification and ageing of tracks. Except for fresh (≤24 old) tracks being slightly more detectable (p2ˆ= 0.77 vs 0.61) and reliably identified (probability of agreement in species identification = 0.92 vs 0.87) than older tracks, we demonstrated that p2ˆ is high and most importantly largely constant across sampling conditions and highly skilled observer pairs. Species identification mismatches between observer pairs were rare (8% for fresh tracks) and limited to specific couples of close species, materializing into confusion rather than directional bias. Where p1ˆ can be accounted for in the derivation of population abundance from track counts (e.g. Formozov–Malyshev–Pereleshin formula), our results relative to p2ˆ validate the use of track surveys for wildlife monitoring in the Kalahari. Our conclusion is potentially applicable to a large range of environments across the globe, especially where observer skills are present among local communities, and conservation can benefit both wildlife and people, fostering their coexistence.

ИССЛЕДОВАНИЕ МИКРОРАСПРЕДЕЛЕНИЯ PU-239 В ПЕЧЕНИ РАБОТНИКОВ ПО «МАЯК» С ИСПОЛЬЗОВАНИЕМ НЕЙТРОННО-ИНДУЦИРОВАННОГО МЕТОДА ИЗМЕРЕНИЯ

Purpose: Conducted research was aimed at studying of microdistribution of 239Pu particles in liver tissues of former MAYAK PA workers. Current research is a continuation of studies of microdistribution of 239Pu particles in lung tissues of former MAYAK PA workers that were conducted earlier and published. Material and methods: Neutron-induced track method was utilized for studying the distribution of sizes of 239Pu nanoparticles. At Southern Urals Biophysics Institute this method was improved, optimized and adapted for studying of plutonium microdistribution in biological tissues. Liver samples studying started in 2020. Samples were chosen from Radiobiology Human Tissue Repository SUBI. Liver samples from Voronezh regional pathology and anatomical bureau and Tobolsk regional hospital #3 were obtained within the search of contemporary liver tissues. Application of liver samples on track detectors and their assembling into plastic box for following irradiation in nuclear reactor at Joint stock company “Institute of Nuclear Materials” was provided similarly to lung samples. Standart pathohistological techniqes were applied. The thickness of liver slides was 5 micrometers. Basic track count was conducted on the results of 36-minute etching. Single tracks and stars were counted. Stars with high density of tracks that exceeded counting abilities were counted on the results of 9-minute etching either directly (if all tracks were distinct) or in accordance with patent for invention RU 2733491 C2 that enables to calculate the number of tracks in a star by distinct peripheral tracks. Results: This study quantitatively compares 239Pu microdistribution in liver of three deceased former Mayak PA workers who were exposed to 239Pu by inhalation and three deceased subjects who had been never employed at Mayak PA (from Ozyorsk, Voronezh, Tobolsk). The comparison is made utilizing neutron-activation method of measurement. The results are compared to the results of less-sensitive autoradiographic method. The study demonstrated that the most of 239Pu activity in liver is concentrated in liver lobules. 239PuO2 nanoparticles found didn’t exceed the size of 20 nm. Track density for three liver samples of subjects who had been never employed at Mayak PA differed for less than two times.

Time-efficient etching of LR-115 SSNTD film for indoor radon, thoron quantification.

LR-115 Solid State Nuclear Track Detector (SSNTD) is commonly utilized for quantifying indoor radon-thoron levels, by tallying the tracks formed in the films by exposure to these gases. Conventionally, sodium hydroxide (NaOH) is used to etch LR-115 films for 90 min at 60°C. However, this study suggests a time-efficient alternative approach utilizing potassium hydroxide (KOH) as the etchant. In an initial investigation, the bulk etch rates of KOH were examined at different normalities and temperatures, revealing that KOH exhibited nearly double the bulk etch rates compared to NaOH. Subsequently, a specially designed controlled experiment was conducted to assess the efficacy of the technique by enumerating the tracks generated in the films. Both etchants demonstrated very similar track counts for identical controlled exposures, indicating the reliability of the method. A consistent behavior was observed in the real-case scenario of LR-115 films exposed indoors to alpha particles from radon and its decay products. In both experiments, the etching with KOH for 45 min gave track densities comparable to standard NaOH etching for 90 min, highlighting the time efficiency of this method. Investigations were carried out into track shape and size features, aspects crucial to the measurement technique, using microscopic imaging of samples treated with both etchants. Strikingly similar track shapes and sizes were observed, affirming the consistency in the track measurement technique. Collectively, these findings suggest that KOH etchant reduces the etching time, presenting itself as a time-efficient method for quantifying radon and thoron track density.

Fission-track age calibration: Phi and Zeta, never the twain shall meet?

The fission-track method was intended to be an autonomous dating method requiring two track counts and a neutron fluence measurement. It became clear that it was beset with methodological problems, and neutron fluence measurements were abandoned in favour of age standards. The coming of mass-spectrometric uranium measurements eliminated the need for irradiations altogether, and stimulated renewed efforts to establish fission-track dating as an autonomous method. This ran into similar problems as before, and recent publications signal a return to age standards. The attempts at independent dating nevertheless revealed that experts agree on the recommended fission constant and the need to consider experimental factors related to the track counts. In spite of that, the renewed reliance on standards makes that fission-track ages are again not independent, but relative, ages with an uncertain meaning, and from their nature untestable.We report an attempt at standardless and standard-based dating of the Durango and Duluth apatites using neutron irradiation and the external detector method. We start from the assumption that neutron fluence measurements are the most precise and accurate achievable in fission-track dating, a consensus on the recommended fission constant, and on the need to consider experimental factors. One factor (ζ0) accounts for the combined experimental influences; ζ0 can be calculated from identifiable contributions related to track registration, etching and counting; ζ0 can also be determined by experiment and calculated from the ages of standards. This gives three separate, consistent, dimensionless calibration factors that, in contrast to Z and ζ, are independent of the neutron fluence or the uranium content of a reference glass; ζ0 remains a personal factor, however.The standardless fission-track ages of the Durango age standard are in agreement with its reference age and the standardless and standard-based ages of the Duluth apatites are consistent with each other. Furthermore, if ζ0 is used to correct conventional Z- (and by implication ζ-) values for experimental effects, the results are consistent with their definitions in terms of physical parameters. Although ζ0 is a personal factor, the small number of measurements to date is consistent with each other. In contrast, annealing corrections based on the fossil and induced confined fission track lengths overestimate the reference age of Durango and produce incompatible standardless and standard-based ages of the dated apatites. We argue that this points to unresolved issues with the relationship between the age and the fossil track length, and suggest a connection with etching.Plots of the single-grain ages against uranium content and radial plots of the data exhibit conspicuous structure. However statistical factors alone account for the nature and magnitude of the observed correlations. This implies that in the case of the Duluth apatites there is no identifiable effect of radiation damage on the single-grain ages despite their high accumulated α-doses and protracted thermal histories. Spurious correlations must be addressed before attempting to interpret the observed data structure in terms of radiation damage, apatite composition or age components.

Artificial intelligent identification of apatite fission tracks based on machine learning

Over the past half century, apatite fission track (AFT) thermochronometry has been widely used in the studies of thermal histories of Earth’s uppermost crust. The acquired thermal histories in turn can be used to quantify many geologic processes such as erosion, sedimentary burial, and tectonic deformation. However, the current practice of acquiring AFT data has major limitations due to the use of traditional microscopes by human operators, which is slow and error-prone. This study uses the local binary pattern feature based on the OpenCV cascade classifier and the faster region-based convolutional neural network model based on the TensorFlow Object Detection API, these two methods offer a means for the rapid identification and measurement of apatite fission tracks, leading to significant improvements in the efficiency and accuracy of track counting. We employed a training dataset consisting of 50 spontaneous fission track images and 65 Durango standard samples as training data for both techniques. Subsequently, the performance of these methods was evaluated using additional 10 spontaneous fission track images and 15 Durango standard samples, which resulted in higher Precision, Recall, and F1-Score values. Through these illustrative examples, we have effectively demonstrated the higher accuracy of these newly developed methods in identifying apatite fission tracks. This suggests their potential for widespread applications in future apatite fission track research.

Efficient and Lightweight Automatic Wheat Counting Method with Observation-Centric SORT for Real-Time Unmanned Aerial Vehicle Surveillance

The number of wheat ears per unit area is crucial for assessing wheat yield, but automated wheat ear counting still faces significant challenges due to factors like lighting, orientation, and density variations. Departing from most static image analysis methodologies, this study introduces Wheat-FasterYOLO, an efficient real-time model designed to detect, track, and count wheat ears in video sequences. This model uses FasterNet as its foundational feature extraction network, significantly reducing the model’s parameter count and improving the model’s inference speed. We also incorporate deformable convolutions and dynamic sparse attention into the feature extraction network to enhance its ability to capture wheat ear features while reducing the effects of intricate environmental conditions. To address information loss during up-sampling and strengthen the model’s capacity to extract wheat ear features across varying feature map scales, we integrate a path aggregation network (PAN) with the content-aware reassembly of features (CARAFE) up-sampling operator. Furthermore, the incorporation of the Kalman filter-based target-tracking algorithm, Observation-centric SORT (OC-SORT), enables real-time tracking and counting of wheat ears within expansive field settings. Experimental results demonstrate that Wheat-FasterYOLO achieves a mean average precision (mAP) score of 94.01% with a small memory usage of 2.87MB, surpassing popular detectors such as YOLOX and YOLOv7-Tiny. With the integration of OC-SORT, the composite higher order tracking accuracy (HOTA) and counting accuracy reached 60.52% and 91.88%, respectively, while maintaining a frame rate of 92 frames per second (FPS). This technology has promising applications in wheat ear counting tasks.

A review of field techniques to estimate lion presence and abundance

Abstract Since the 1990s, the number of lions (Panthera leo) has declined by about 43% across African range states. Reliable lion population estimates can contribute to effective management and inform local and international conservation policies. Though many survey methods have been used to estimate lion abundance, an assessment of their usefulness and effectiveness is lacking. We reviewed and identified available methods used to estimate lion populations, then investigated their feasibility, cost‐effectiveness and suitability to achieve primary objectives. We identified 12 methods in two categories: direct (minimum counts, call‐in surveys, capture–recapture, opportunistic sightings, remote cameras, distance‐based and aerial counts) and indirect (track counts, secondary information, prey‐based, genetic samples and roar counts). We identified 48 studies (57%) in which lion population estimation was not repeatable. Additionally, 38% did not provide any measure of precision. About 28% of the studies were conducted specifically for management purposes, while remaining studies focused on population estimates and dynamics. Because data from these studies have influenced local management and global lion conservation policies, we recommend basing future policies and management on repeatable, precise and accurate population estimates supported by appropriate and cost‐effective experimental designs. In most cases, these estimates are obtained through actual lion counts derived from direct observation methods including call‐in, capture–recapture and remote camera surveys or through reliable animal signs such as genetic samples.

Long-term monitoring in the boreal forest reveals high spatio-temporal variability among primary ecosystem constituents

The boreal forest, the world’s largest terrestrial biome, is undergoing dramatic changes owing to anthropogenic stressors, including those of climate change. To track terrestrial ecosystem changes through space and time, robust monitoring programs are needed that survey a variety of ecosystem constituents. We monitored white spruce (Picea glauca) cone crops, berry (Empetrum nigrum, Shepherdia canadensis) production, above-ground mushroom abundance, and the abundance of small mammals (Clethrionomys rutilus,Peromyscus maniculatus), North American red squirrels (Tamiascirus hudsonicus), snowshoe hares (Lepus americanus), and carnivores (Lynx canadensis,Canis latrans,Vulpes vulpes,Martes americana,Mustela erminea) across 5 sites in the Yukon, Canada. Monitoring began in 1973 at Lhù’ààn Mân’ (Kluane Lake) and additional protocols were added until a complete sequence was fixed in 2005 at all 5 sites and continued until 2022. White spruce cone counts show mast years at 3–7-year intervals. Ground berries and soapberry counts were highly variable among sites and counts did not correlate among sites or between years for different species. Red-backed voles showed clear 3–4-year cycles at Kluane and probably at the Mayo and Watson Lake sites, but showed only annual cycles in Whitehorse and Faro. Snowshoe hares fluctuated in 9–10-year cycles in a travelling wave, peaking one year earlier at Watson Lake but in synchrony at all other sites, with no clear sign of peak density changing or cyclic attenuation over the last 50 years. Red squirrel numbers at Kluane exhibit marked inter-year variability, driven mainly by episodic white spruce cone crops and predation from Canada lynx and coyotes as hare densities undergo cyclic decline. Snow track counts to index mammalian predators have been conducted on our Kluane and Mayo sites, indicating that lynx numbers rise and fall with a 1–2-year lag at these two sites, tracking the hare cycle. Coyotes and lynx at Kluane peak together following the hare cycle, but coyote counts are also depressed during deep snow years. To summarize, we noted considerable inter-site variability in the population dynamics of many boreal forest ecosystem constituents, but the keystone species (snowshoe hare, Canada lynx) exhibit remarkably similar population trends across the region. We continue to monitor wildlife abundance, cone crops, berry production, and mushroom biomass to determine changes associated with increasing temperature and fluctuating rainfall. The Yukon boreal forest is changing as climate shifts, but the changes are slow, variable across sites, taxa specific, and of uncertain predictability.

Nighttime Rainy Season Traffic Analysis: Vehicle Detection, Tracking, and Counting with YOLOv8 and DeepSORT

This research focuses on developing a reliable computer vision system for accurately tracking traffic density in India during the rainy season. The system uses deep learning-based techniques to handle the difficulties associated with vehicle detection and tracking. The three modules are vehicle detection, tracking, and vehicle counting. Vehicles are initially identified using the YOLOv8 algorithm, a state-of-the-art deep learning detector. Subsequently, the DeepSORT algorithm is utilized for multi-object tracking to ensure accurate and robust tracking of various objects, including cars, buses, trucks, bikes, and pedestrians. The importance of accurate vehicle counting and speed measurement is emphasized, especially during bad weather. An independently compiled dataset of Indian rainy conditions is used to assess the proposed computer vision system. The outcomes demonstrate the system's capability to accurately identify, track, count, and estimate the speeds of vehicles. These features offer insightful information for traffic analysis, including flow monitoring, congestion detection, and other associated traffic challenges. This study makes a contribution to the field of computer vision-based traffic monitoring and offers potential applications in transportation management systems under challenging weather conditions.

Improving the network architecture of YOLOv7 to achieve real-time grading of canola based on kernel health

The occurrence of heated and immature canola kernels caused by excessive drying and frost damage is undesired by grain buyers due to lower oil yield and diminished market value. The current grading process is visually examining each kernel's endosperm color and counting the damaged seeds. As this process is time-consuming, laborious, and prone to errors, this study proposes an automated grading technique based on object detection, multi-object tracking, and counting. The detection task was achieved via an improved YOLOv7 network (YOLOv7_ours) that was modified to increase its performance in accurately identifying small objects by adding two convolutional block attention modules in the neck region and decrease its computational complexity (cost) and size by substituting convolutional layers with ghost layers in all the Efficient Layer Aggregation Networks modules, and in the Spatial Pyramid Pooling Cross Stage Partial module present in YOLOv7. The weights of the trained network were fed to the ByteTrack multiple object tracker to track the detections frame-by-frame in a video feed. The unique identities generated by the tracker for each detected object of interest were then used to count the number of defects using a line cross algorithm. The mean average precision (mAP@0.5) obtained after training the YOLOv7_ours model was 1.02% better and its cost and size were 32.1% and 37.1% lower than the baseline YOLOv7 model. In a test video, the overall model achieved a multi-object tracking accuracy and counting accuracy of 84.8% and 93.9%, respectively. This three-stage model can be readily deployed in an edge device for accurate and real-time grading of canola kernels by grain buyers.

Visible and Infrared Image Fusion Using Deep Learning.

Visible and infrared image fusion (VIF) has attracted a lot of interest in recent years due to its application in many tasks, such as object detection, object tracking, scene segmentation, and crowd counting.In addition to conventional VIF methods, an increasing number of deep learning-based VIF methods have been proposed in the last five years.Different types of methods, such as CNN-based, autoencoder-based, GAN-based, and transformer-based methods, have been proposed.Deep learning-based methods have undoubtedly become dominant methods for the VIF task.However, while much progress has been made, the field will benefit from a systematic review of these deep learning-based methods.In this paper we present a comprehensive review of deep learning-based VIF methods.We discuss motivation, taxonomy, recent development characteristics, datasets, and performance evaluation methods in detail.We also discuss future prospects of the VIF field.This paper can serve as a reference for VIF researchers and those interested in entering this fast-developing field.

Estimating mammal density from track counts collected by Indigenous Amazonian hunters

Monitoring programs must produce reliable estimates of animal population density to effectively guide management decisions and conservation action. Popular methods used to estimate density rely on visual encounters which are difficult to obtain for many large-bodied mammals in tropical forests. This study uses the Formozov-Malyshev-Pereleshin (FMP) method to produce density estimates for four terrestrial mammals based on track counts collected by Indigenous hunters in the Amazon. The focal species include collared peccary (Peccari tajacu), white-lipped peccary (Tayassu pecari), lowland tapir (Tapirus terrestris), and jaguar (Panthera onca). For each species, track detections were greater than visual detections and were found on more transects with a fewer number of surveys. Density estimates were produced for each focal species in hunted and nonhunted sites. These results were compared against the median of 84 density estimates located in the literature that differentiated between hunted and nonhunted sites in the Amazon. Results demonstrate that the FMP method is a reasonable and cost-efficient method. This method can directly engage Indigenous and other local peoples in research, as opposed to replacing their skills with technologies or professional biologists. Overall, this method complements traditional transect surveys and can improve our understanding and management of wildlife across the tropics.

Citrus Identification and Counting Algorithm Based on Improved YOLOv5s and DeepSort

A method for counting the number of citrus fruits based on the improved YOLOv5s algorithm combined with the DeepSort tracking algorithm is proposed to address the problem of the low accuracy of counting citrus fruits due to shading and lighting factors in videos taken in orchards. In order to improve the recognition of citrus fruits, the attention module CBAM is fused with the backbone part of the YOLOv5s network, and the Contextual Transformer self-attention module is incorporated into the backbone network; meanwhile, SIoU is used as the new loss function instead of GIoU to further improve the accuracy of detection and to better keep the model in real time. Then, it is combined with the DeepSort algorithm to realize the counting of citrus fruits. The experimental results demonstrated that the average recognition accuracy of the improved YOLOv5s algorithm for citrus fruits improved by 3.51% compared with the original algorithm, and the average multi-target tracking accuracy for citrus fruits combined with the DeepSort algorithm was 90.83%, indicating that the improved algorithm has a higher recognition accuracy and counting precision in a complex environment, and has a better real-time performance, which can effectively achieve the real-time detection and tracking counting of citrus fruits. However, the improved algorithm has a reduced real-time performance and has difficulty in distinguishing whether or not the fruit is ripe.

Computational and experimental determination of slow neutron flux by using Li-6 foil and nuclear track detector

In the present study, slow neutron flux was determined with an experimental method based on 6Li(n, t)4He reaction. 6Li coated foils were mounted on track etch detectors without any gap and they were irradiated with an Am–Be neutron source for 6 h. The track detectors were affixed on a polyethylene plate which has a thickness of 2.0 cm for neutron moderation. Following irradiation, the track detectors were etched, the tritium tracks were counted with an in-house developed track counting system, and the total number of reactions was computed. The difference in particle ranges and track diameters on the Cr-39 detector allowed for the precise distinguishing of tritium, alpha, and neutron particles. The neutron spectrum at the 6Li coated foil position was simulated with MCNP5 using Evaluated Nuclear Data File (ENDF) cross sections of 6Li(n, t)4He reaction. The total reaction numbers obtained with experimental and computational methods are compatible with each other.