In order to solve the problem of cow face target recognition accuracy,a Yolov5 target detection algorithm model based on the training of lightweight bovine face data set was adopted to recognize bovine face targets in the acquired cow images covering complex backgrounds,supported by deep learning image processing technology.Based on Yolov5 model,the detection ability of small bovine face objects was improved.CBAM plug and play attention mechanism was introduced to enhance the network's ability to perceive meaningful areas and reduce the influence of interference information such as complex environmental noise in the background of cow barn.The fusion of BiFPN weighted bidirectional feature pyramid network structure could effectively merge the deep and shallow features of individual bovine faces,and improve the detection ability of the network for objects containing large and small cow faces in images.The average accuracy of bovine face detection was 0.934,supported by small sample cow face data set.The results showed that this research could effectively detect the cow face target in actual production.
关键词
深度学习/目标检测/牛脸检测/Yolov5s
Key words
deep learning/object detection/cow face detection/Yolov5
维普
万方数据