基于局部感知的肉牛多行为检测与统计方法

Multi Behavior Detection and Statistical Method for Beef Cattle Based on Local Perception

摘要:

针对目前肉牛行为研究主要集中在基本行为识别，缺乏对复杂行为间的局部感知，本文对肉牛基本行为下的精细行为进行识别研究，提出基于改进YOLO v8的肉牛多行为检测与统计方法。采集肉牛行为图像，构建包括站立、躺卧、进食、饮水等基本行为和回舔、行走、探究、甩尾等精细行为的肉牛行为数据集；选用YOLO v8n-P2作为基础模型，以增强模型对犊牛的检测能力；设计C2f-PPA特征提取结构，捕捉肉牛不同行为间局部特征变化；采用交互策略对YOLO v8检测头改进，构建任务动态对齐检测头TDADH；采用MPDIoU损失函数，解决了CIoU局限性问题。基于检测结果对全天候时间内肉牛行为统计，对不同遮挡环境下统计结果进行图像可视化显示。结果表明，PTPM-YOLO v8模型识别全部（8种）行为的精确率为91.0%，召回率为87.9%，平均精度均值为94.3%。与原模型YOLO v8n相比，PTPM-YOLO v8n的mAP@0.5提高3.0个百分点，参数量降低23.3%；识别全部行为和基本行为的mAP@0.5分别提升3.0、2.4个百分点。本文实现了养殖环境下肉牛精细行为准确识别，可为肉牛多行为监测提供参考。

Abstract:

Given the current research on beef cattle behavior, which mainly focused on basic behavior recognition and lacked local perception of complex behaviors, the identification of fine behaviors under basic behaviors of beef cattle was researched. A multi-behavior detection and statistical method based on YOLO v8 for beef cattle was proposed. Cattle behavior images were collected by cameras to build a comprehensive dataset that included fundamental behaviors such as standing, lying down, feeding, and drinking, as well as fine behaviors like licking, walking, searching, and tail flicking. YOLO v8n-P2 was selected as the basic model to enhance the ability of the model to detect calves;the feature extraction structure of C2F-PPA was designed;the YOLO v8 detection head was improved by interactive strategy, and the TDADH was constructed. MPDIoU loss function was used to address limitations associated with CIoU. Subsequently, statistical analysis of cattle behaviors based on detection results throughout the day was conducted;these results were visually displayed for various occlusion environments. The experimental results showed that the PTPM-YOLO v8n model achieved precision rate of 91.0%, a recall rate of 87.9%, and a mAP@0.5 score of 94.3% in recognizing all eight behaviors tested. Compared with the original model YOLO v8n, the mAP@0.5 of PTPM-YOLO v8n was increased by 3.0 percentage points, and the parameter number was decreased by 21.9%. which identified all behaviors and basic behaviors, the mAP@0.5 was increased by 3.0 and 2.4 percentage points, respectively. The method presented can accurately identify fine behaviors of beef cattle under farming conditions, providing a reference for multi-behavior monitoring of beef cattle.