ASPP-YOLOv5: A study on constructing pig facial expression recognition for heat stress

Abstract

In the global pig farming industry, the decrease in growth rate of fattening pigs during hot seasons is a common and challenging issue. Especially in hot and humid environments, pigs are prone to heat stress, which can even lead to death in severe cases, causing significant economic losses to the farming industry. Therefore, timely detection of heat stress and taking appropriate measures is of great importance for saving the pigs. The ASPP-YOLOv5 model for pig heat stress facial expression recognition enhances the feature extraction capability by incorporating coordinated attention mechanisms. The CReToNeXt module is used to improve feature extraction and fusion, thereby enhancing the model's representational capacity and performance. The ASPP module better adapts to the multi-scale features of pig heat stress expressions, improves the network's perception of different-scale expression features, and enhances the model's recognition ability for pig heat stress expressions. The mean Average Precision(mAP) reached 93.2 %, representing a 7 % improvement compared to the YOLOv5s model. It also outperformed the Faster R-CNN and YOLOv4 models by achieving a remarkable 47.7 % and 48.8 % increase, respectively, in mAP. This model can be applied to a pig farm's heat stress unmanned and contactless real-time monitoring system, enabling timely warning for heat stress.