Joint Feature Target Detection Algorithm of Beak State Based on YOLOv5

Accurate grasp of chicken body temperature can effectively improve the success rate of caged chicken breeding, by monitoring the number of open-mouthed chickens as a percentage of the total number of chickens and can directly determine whether the chicken body temperature is appropriate. There is no relevant solution to this requirement at present, so this paper proposes a joint feature target detection algorithm based on YOLOv5 to detect the opening and closing state of the chicken mouth. The algorithm improves the YOLOv5 network in the following ways: 1) The improved ResC module is used to reconstruct the backbone network of YOLOv5, which diversifies feature scales and enhances the ability of target feature extraction; 2) Integrate the Transformer module with the four-layer feature pyramid to expand the range of feature fusion and improve the accuracy of feature extraction; 3) The joint feature verification(JFV) module is designed to improve the detection accuracy of small targets by adopting the idea of joint verification of small targets and large targets. Finally, the improved network is used to detect the opening and closing state of the chicken beak on the test set, which is derived from the actual cage chicken breeding environment. The results show that the average accuracy (mAP) of the improved RJ-YOLOv5 algorithm is 85.6%, and the detection accuracy is 7.1% higher than the YOLOv5 algorithm; The video detection frame rate reaches 69 FPS, which can meet the requirements of real-time monitoring of chicken farms.