基于改进YOLOv10的牛只种类与行为识别

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中图分类号：TP183；S818.9 文献标志码：A 文章编号：1001-411X（2025）06-0832-11

Abstract：【Objective】To achieve real-time monitoring of cattle activity status in precision breeding，help farmers promptly identify cattle abnormal behaviors，and provide support for cattle feed allocation， disease monitoring，and breeding management. 【Method】 This study modified the YOLO v10 model into LDCMYOLO vl0n，and based on this， conducted detection on cattle species and behaviors. The specific improvements were as follows： First， the C2f-LSKA structure was adopted in the Backbone of YOLO v10 to enhance the feature extraction capability of the model. Second， the DySample upsampling operator was introduced to effectively capture subtle changes in images and dense semantic information， avoiding the problems of image blurring and limited receptive field in traditional upsampling methods. Meanwhile， the PSA of YOLO v10 was replaced withthe CloFormer attention mechanism to better distinguish catle features from background noise and accurately identify smalltargets.In addition，the multi-scale dilated attention mechanism （MSDA） was added to enhance theaggregated semantic informationat various scales within the receptive field and effectively reduce the redundancy of theself-attention mechanism.Finally，the Inner-IoU loss function was used to address the issuethat theordinaryIoU loss function could not flexibly adjust losscalculation according to the target scale. 【Result】 On the cattle behavior dataset， the mAP @0.50 of the LDCM-YOLO v10n model increased by 15.4， 10.7， 12.0， 8.4， 7.9 and 5.1 percentage points compared with YOLO v3， YOLO v5， YOLO v6， YOLO v8n， YOLO v9 and YOLO v10n model， respectively. Meanwhile， on the cattle species dataset， the mAP @0.50 of the LDCM-YOLO v10n model increased by 32.4，11.9，10.4， 9.5， 9.0 and 6.4 percentage points compared with the aforementioned models，respectively. 【Conclusion】The LDCM-YOLO v10n model demonstrates excelent performance in catle behavior and species detection， providing a strong technical support for precision breeding.

Key words： Convolutional neural network; Catle; Image recognition; YOLO v10 model; Artificial intellgence

近年来，畜牧业在我国农业经济中的重要性不断增强，已成为农业的支柱产业之一[1]。（剩余16913字）