基于改进YOLO v4的生猪耳根温度热红外视频检测方法

Detection Method of Pig Ear Root Temperature Based on Improved YOLO v4

基于热红外视频的生猪体温检测过程中,视频中保育期生猪头部姿态变化大,且耳根区域小,导致头部和耳根区域定位精度低,影响生猪耳根温度的精准检测。针对以上问题,本文提出了一种基于改进YOLO v4(Mish Dense YOLO v4,MD-YOLO v4)的生猪耳根温度检测方法,构建了生猪关键部位检测模型。首先,在CSPDarknet-53主干网络中,添加密集连接块,以优化特征转移和重用,并将空间金字塔池化(Spatial pyramid pooling,SPP)模块集成到主干网络,进一步增加主干网络感受野;其次,在颈部引入改进的路径聚合网络(Path aggregation network,PANet),缩短多尺度特征金字塔图的高、低融合路径;最后,网络的主干和颈部使用Mish激活函数,进一步提升该方法的检测精度。试验结果表明,该模型对生猪关键部位检测的mAP为95.71%,分别比YOLO v5和YOLO v4高5.39个百分点和6.43个百分点,检测速度为60.21 f/s,可满足实时检测的需求;本文方法对热红外视频中生猪左、右耳根温度提取的平均绝对误差分别为0.26℃和0.21℃,平均相对误差分别为0.68%和0.55%。结果表明本文提出的基于改进YOLO v4的生猪耳根温度检测方法,可以应用于热红外视频中生猪关键部位的精准定位,进而实现生猪耳根温度的准确检测。

In the process of pig body temperature detection based on thermal infrared video,the head posture of pigs in the nursery period changes greatly,and the ear base area was small,resulting in low positioning accuracy of the head and ear base area,which affected the accurate detection of pig ear base temperature.In view of the above problems,an improved YOLO v4(Mish Dense YOLO v4,MD-YOLO v4) method for detecting the temperature of pig ears was proposed and a detection model for key parts of pigs was built.Firstly,in the CSPDarknet-53 backbone network,dense connection blocks were added to optimize feature transfer and reuse,and the spatial pyramid pooling(SPP) module was integrated into the backbone network to further increase the backbone network receptive field;secondly,an improved path aggregation network(PANet) was introduced in the neck to shorten the high and low fusion paths of the multi-scale feature pyramid graph;finally,the Mish activation function was used in the backbone and neck of the network to further improve the detection accuracy of the method.The test results showed that the mAP of the model for the detection of key parts of live pigs was 95.71%,which was 5.39 percentage points and 6.43 percentage points higher than that of YOLO v5 and YOLO v4,respectively,and the detection speed was 60.21 f/s,which can meet the requirements of real-time detection.The average absolute errors of the left and right ear root temperature extraction of pigs in the thermal infrared video were 0.26℃ and 0.21℃,respectively,and the average relative errors were 0.68% and 0.55%,respectively.The results showed that the pig ear root temperature detection method based on the improved YOLO v4 proposed can be applied to the accurate positioning of the key parts of pigs in thermal infrared video,thereby realizing the accurate detection of pig ear root temperature.