基于彩色与热红外图像信息融合的肉鸡死鸡识别方法OA北大核心

In order to improve the accuracy of dead broiler detection in large-scale broiler farms,based on color images and thermal infrared images,two-stage and one-stage dead broiler detection methods for broilers were proposed,respectively.In the two-stage method,the YOLO v11-seg network was firstly used to segment broilers in color images to obtain broiler mask coordinates;then individual broiler thermal infrared images were extracted and classified by using the YOLO v8-cls classification network.In the one-stage method,G-channel replacement fusion images,weighted fusion images,wavelet transform fusion images,and frequency domain transform fusion images were constructed based on color images and registered thermal infrared images.Multi-source fusion image datasets were used to build a dead broiler detection model based on the YOLO v11s object detection network.The results showed that in the two-stage dead broiler detection method,the mAP of broiler instance segmentation was 94.2％,and the classification accuracy of individual broiler thermal infrared images was 99.4％.In the one-stage dead broiler detection method,the model built based on wavelet transform fusion images achieved the highest detection accuracy,with mAP of 93.0％.Compared with the two-stage method,the one-stage detection method had a higher precision rate of 92.3％on the public test set,faster inference speed(6.1 ms/f),and easier to be deployed.Analysis of the temperature distribution of individual broiler thermal infrared images indicated that there were significant differences in body surface temperature distribution between low-age and high-age broilers.The dead broiler detection method proposed can accurately identify dead broilers in the harsh imaging environment under high-density breeding,and it can provide a technical reference for the death detection of other livestock and poultry.