Improved Yolov8 Algorithm for Cow Recognition Based on Soft NMS
DOI:
https://doi.org/10.53469/jtpms.2025.05(6).05Keywords:
Object detection, Non maximum suppression algorithm, YoloAbstract
Currently, artificial intelligence and IoT technology are widely used in the agricultural field, and China's smart agriculture is steadily developing. The introduction of computer vision technology is gradually freeing farms and other animal recognition systems from the reliance on sensors in traditional monitoring systems. However, animal recognition in dense scenes has the characteristics of small space, large quantity, large individual volume, and serious occlusion and adhesion problems, making it difficult to accurately identify every animal in the animal population. This article uses the YOLOv8 model to implement cow recognition and replaces traditional non maximum suppression algorithms with the Soft NMS algorithm. By comparison, the individual recognition of large animals with severe occlusion has been solved, laying the foundation for the next stage of animal behavior recognition.
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