Efficient Livestock Detection in Grazing Areas Based on Enhanced Lightweight Deep Network

Realizing big data to manage livestock requires real-time monitoring of livestock, but real-time monitoring of livestock is easily interfered by large changes in target size, lighting, environmental factors, etc., so it is difficult to detection, and existing livestock detection algorithms have the problem of poor robustness.An object detection network called E-YOLOv4-tiny is proposed based on enhanced YOLOv4-tiny, which adopts a pyramid network with multi-scale feature fusion, taking into account shallow local detail features and deep semantic information to solve the problem of livestock size fluctuation in pastoral areas.The number of backbone network parameters is reduced by improving the residual structure to accommodate embedded platform requirements.A new composite clustering algorithm is introduced to design anchor frames to improve the accuracy of the algorithm under the premise of ensuring portability.Finally, according to the characteristics of a pastoral environment, a new Compound Muti-channel Attention(CMA) mechanism is proposed to improve the poor accuracy of the target detection network and enhance the robustness of the algorithm.Experimental results show that the mean Average Precision(mAP) of the E-YOLOv4-tiny algorithm is 0.878 9, and the frame rate is 32 frame/s, and it's mAP is 9.32% higher than that of the traditional YOLOv4-tiny algorithm while maintaining almost the same detection rate.