基于坐标注意力机制和YOLO v5s模型的山羊脸部检测方法

Goat Face Detection Method by Combining Coordinate Attention Mechanism and YOLO v5s Model

山羊的脸部检测对羊场的智能化管理有着重要的意义。针对实际饲养环境中,羊群存在多角度、分布随机、灵活多变、羊脸检测难度大的问题,以YOLO v5s为基础目标检测网络,提出了一种结合坐标信息的山羊脸部检测模型。首先,通过移动设备获取舍内、舍外、单头以及多头山羊的图像并构建数据集。其次,在YOLO v5s的主干网络融入坐标注意力机制,以充分利用目标的位置信息,提高遮挡区域、小目标、多视角样本的检测精度。试验结果表明,改进YOLO v5s模型的检测精确率为95.6%,召回率为83.0%,mAP0.5为90.2%,帧速率为69 f/s,模型内存占用量为13.2 MB;与YOLO v5s模型相比,检测精度提高1.3个百分点,模型所占内存空间减少1.2 MB;且模型的整体性能远优于Faster R-CNN、YOLO v4、YOLO v5s模型。此外,本文构建了不同光照和相机抖动的数据集,来进一步验证本文方法的可行性。改进后的模型可快速有效地对复杂场景下山羊的脸部进行精准检测及定位,为动物精细化养殖时目标检测识别提供了检测思路和技术支持。

Animal face detection is of great significance to the intelligent management of animal farm.At present,goats have the characteristics of multi angle,random distribution and flexibility in the actual feeding environment,which greatly increases the difficulty of goat face detection.Therefore,a goat face detection model combined with coordinate information was proposed based on YOLO v5s target detection network.Firstly,indoor,outdoor,single and multiple goat images were obtained by using mobile devices to build sample data sets.Secondly,coordinate attention mechanism(CA)was integrated into the backbone network of YOLO v5s to make full use of target position information and improve the target recognition accuracy in the occluded area,small target and multi view sample images.The proposed YOLO v5s-CA based approach achieved a precision of 95.6%,a recall of 83.0%,an mAP0.5 of 90.2%,a frame rate of 69f/s and a model size of 13.2MB.Compared with that of the original YOLO v5s model,the detection precision of YOLO v5s-CA was increased by 1.3 percentage points,and the memory space was reduced by 1.2MB.And the overall performance of the YOLO v5s-CA was better than that of the Faster R-CNN,YOLO v4 and YOLO v5s.Experimental results showed that the proposed YOLO v5s-CA approach can improve the detection precision of occluding and small targets by introducing target coordinate information.In addition,datasets with different lighting and camera shake were simulated and constructed to further verify the feasibility of the proposed method.Overall,the proposed deep learning-based goat face detection approach can quickly and effectively detect and locate goat faces in complex scenes,providing detection ideas and technical support for target detection and recognition in intelligent animal farm.