1. What Does Region Counting Involve? Region counting is a computational method utilized to ascertain the quantity of objects within a specific area in recorded video or real-time streams. This technique finds frequent application in image processing, computer vision, and pattern recognition, facilitat...
remote: Enumerating objects: 248, done. remote: Counting objects: 100% (248/248), done. remote: Compressing objects: 100% (223/223), done. remote: Total 248 (delta 36), reused 102 (delta 20), pack-reused 0 Receiving objects: 100% (248/248), 620.63 KiB | 4.89 MiB/s, done. Resol...
However, the traditional YOLOv8 detection algorithm faces significant challenges in detecting small objects in UAV imagery, primarily due to a high missed detection rate and an excessive number of parameters. To address these issues, this paper introduces an enhanced small object detection approach, ...
remote: Counting objects: 100% (4583/4583), done. remote: Compressing objects: 100% (1270/1270), done. remote: Total 4583 (delta 2981), reused 4576 (delta 2979), pack-reused 0 Receiving objects: 100% (4583/4583), 23.95 MiB | 1.55 MiB/s, done. Resolving deltas: 100% (2981/2981)...
YOLOv8 OBB Models:The introduction of Oriented Bounding Box models in YOLOv8 marks a significant step in object detection, especially for angled or rotated objects, enhancing accuracy and reducing background noise in various applications such as aerial imagery and text detection. ...
百香果产量的精确估计对于果园的有效管理至关重要,但它带来了诸如遮挡、光线变化和相机抖动等挑战,这可能导致漏检、错误检测和重复计数小果等问题。在本研究中,提出了一种鲁棒的计算机视觉算法YOLOv8n + OC-SORT + CRCM (Central Region Counting Method)来完成百香果的检测、跟踪和产量估计三个任务。首先,比较了...
[Object counting](../guides/object-counting.md) in regions with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) involves precisely determining the number of objects within specified areas using advanced [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv...
Additionally, we analyze the detection difficulty and potential biases for different objects in the SeaDronesSee dataset based on the experimental results. Method Approach overview As illustrated in Fig. 1, the YOLOv8 model architecture is divided into three main sections: the Backbone section, the ...
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Choose Image / Video / Webcam / Folder (Batch ) in the menu bar on the left to detect objects.2. Change Models / Hyper Parameters dynamicallyWhen the program is running to detect targets, you can change models / hyper ParametersSupport changing model in YOLOv5 / YOLOv7 / YOLOv8 / ...