Video-based vehicle detection and counting play a critical role in managing transport infrastructure. Traditional image-based counting methods usually involve two main steps: initial detection and subsequent tracking, which are applied to all video frames, leading to a significant increase in computational complexity. To address this issue, this work presents an alternative and more efficient method for vehicle detection and counting. The proposed approach eliminates the need for a tracking step and focuses solely on detecting vehicles in key video frames, thereby increasing its efficiency. To achieve this, we developed a system that combines YOLO, for vehicle detection, with Visual Rhythm, a way to create time-spatial images that allows us to focus on frames that contain useful information. Additionally, this method can be used for counting in any application involving unidirectional moving targets to be detected and identified. Experimental analysis using real videos shows that the proposed method achieves mean counting accuracy around 99.15% over a set of videos, with a processing speed three times faster than tracking based approaches.
Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models in computational pathology (CPath). However, automatic recognition of different nuclei is faced with a major challenge in that there are several different types of nuclei, some of them exhibiting large intraclass variability. In this work, we propose an approach that combine Separable-HoverNet and Instance-YOLOv5 to indentify colon nuclei small and unbalanced. Our approach can a…
Maize Seedling Detection Dataset (MSDD): A Curated High-Resolution RGB Dataset for Seedling Maize Detection and Benchmarking with YOLOv9, YOLO11, YOLOv12 and Faster-RCNN
Department of Electrical Engineering and Computer Science, University of Missouri（电气工程与计算机科学系，密苏里大学）
Developing a Hybrid Convolutional Neural Network for Automatic Aphid Counting in Sugar Beet Fields
Tobacco Research Institute, Chinese Academy of Agricultural Sciences（中国农业科学院烟草研究所）；University of Lincoln（林肯大学）；British Beet Research Organisation（英国甜菜研究组织）；Lincoln Agri-Robotics Centre, Lincoln Institute for Agri-Food Technology, University of Lincoln（林肯大学林肯农业食品技术研究所林肯农业机器人中心）；Lincoln Centre for Autonomous Systems (L-CAS), University of Lincoln（林肯大学林肯自主系统中心）
University of British Columbia（不列颠哥伦比亚大学）；Agriculture and Agri-Food Canada（加拿大农业与农业食品部）
Tree fruit breeding is a long-term activity involving repeated measurements of various fruit quality traits on a large number of samples. These traits are traditionally measured by manually counting the fruits, weighing to indirectly measure the fruit size, and fruit colour is classified subjectively into different color categories using visual comparison to colour charts. These processes are slow, expensive and subject to evaluators' bias and fatigue. Recent advancements in deep learning can help automate this process. A method was developed to automatically count the number of sweet cherry f…
QuantCube Technology（QuantCube科技公司）；Univ. Bretagne Sud（南布列塔尼大学）；Centre National d'Etudes Spatiales（法国国家空间研究中心）
Detection of new infrastructures (commercial, logistics, industrial or residential) from satellite images constitutes a proven method to investigate and follow economic and urban growth. The level of activities or exploitation of these sites may be hardly determined by building inspection, but could be inferred from vehicle presence from nearby streets and parking lots. We present in this paper two deep learning-based models for vehicle counting from optical satellite images coming from the Pleiades sensor at 50-cm spatial resolution. Both segmentation (Tiramisu) and detection (YOLO) architect…
University of Missouri-Columbia（密苏里大学哥伦比亚分校）；WSP USA（WSP美国公司）
The rapid advancement in the field of deep learning and high performance computing has highly augmented the scope of video based vehicle counting system. In this paper, the authors deploy several state of the art object detection and tracking algorithms to detect and track different classes of vehicles in their regions of interest (ROI). The goal of correctly detecting and tracking vehicles' in their ROI is to obtain an accurate vehicle count. Multiple combinations of object detection models coupled with different tracking systems are applied to access the best vehicle counting framework. The …
Fondazione Istituto Italiano di Tecnologia (IIT)（意大利技术研究院）；Istituto per le Applicazioni del Calcolo del Consiglio Nazionale delle Ricerche（意大利国家研究委员会计算应用研究所）；Università degli Studi Roma tre（罗马第三大学）；Polish Academy of Sciences（波兰科学院）；Harvard University（哈佛大学）
Deep neural networks are rapidly emerging as data analysis tools, often outperforming the conventional techniques used in complex microfluidic systems. One fundamental analysis frequently desired in microfluidic experiments is counting and tracking the droplets. Specifically, droplet tracking in dense emulsions is challenging as droplets move in tightly packed configurations. Sometimes the individual droplets in these dense clusters are hard to resolve, even for a human observer. Here, two deep learning-based cutting-edge algorithms for object detection (YOLO) and object tracking (DeepSORT) ar…
Phys-3D: Physics-Constrained Real-Time Crowd Tracking and Counting on Railway Platforms
Visual Computing Group, Humboldt University Berlin（柏林洪堡大学视觉计算组）；Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, HHI（弗劳恩霍夫电信研究所，海因里希·赫兹研究所，HHI）
CountPath: Automating Fragment Counting in Digital Pathology
Instituto Superior Técnico（高等技术学院）；INESC-ID；Faculdade de Engenharia da Universidade do Porto（波尔图大学工程学院）；INESC-TEC；IMP Diagnostics（IMP诊断公司）；Portuguese Oncology Institute of Porto（波尔图葡萄牙肿瘤研究所）
A unified three-branch deep learning framework for automated detection and counting of wildlife in aerial imagery
School of Geospatial Engineering and Science, Sun Yat-Sen University（中山大学地理空间工程与科学学院）；Faculty of Geo-Information Science and Earth Observation, University of Twente（特温特大学地球信息科学与对地观测学院）；Boma and Badingilo National Parks, African Parks（博马和巴丁吉洛国家公园，非洲公园组织）；Department of Ecology and Evolutionary Biology, Princeton University（普林斯顿大学生态与进化生物学系）；Lolldaiga Hills Research Programme（洛尔迪加山研究项目）；School of Biological, Earth & Environmental Sciences, Environmental Research Institute, University College Cork（科克大学环境研究所生物、地球与环境科学学院）
Towards Practical Precision Agriculture: Real-Time Fruit Detection and Video Analytics on Embedded Edge Hardware
Faculty of Computer Science and Engineering, University Ss Cyril and Methodius（圣西里尔和迪乌斯大学计算机科学与工程学院）；Faculty of Agricultural Sciences and Food, University Ss Cyril and Methodius（圣西里尔和迪乌斯大学农业科学与食品学院）
本研究提出在Jetson Orin Nano Super上集成YOLO26s与DeepStream的实时水果检测跟踪计数框架，验证了FP16精度下高效能与低能耗，并指出采集几何对跟踪稳定性的关键影响。
KU Leuven（鲁汶大学）；PXL Smart-ICT, Hogeschool PXL（PXL Smart-ICT，PXL大学学院）
Shopping behaviour analysis through counting and tracking of people in shop-like environments offers valuable information for store operators and provides key insights in the stores layout (e.g. frequently visited spots). Instead of using extra staff for this, automated on-premise solutions are preferred. These automated systems should be cost-effective, preferably on lightweight embedded hardware, work in very challenging situations (e.g. handling occlusions) and preferably work real-time. We solve this challenge by implementing a real-time TensorRT optimized YOLOv3-based pedestrian detector,…
Towards Edge-Cloud Architectures for Personal Protective Equipment Detection
Orange Innovation, Orange Polska S.A.（Orange创新中心，波兰Orange股份公司）；Systems Research Institute Polish Academy of Sciences（波兰科学院系统研究所）；Warsaw Univesity of Technology（华沙理工大学）
