Dead Duck Recognition Method Based on Improved Mask R-CNN

Traditional manual methods for identifying dead ducks within large-scale stacked cage poultry houses have proven to be inefficient, labor-intensive, and costly. Focusing on stacked cage housing for meat ducks, a deep learning-based method was proposed for dead duck recognition. To collect the necessary dataset, a specialized autonomous inspection system tailored for meat duck housing within three-dimensional stacked environments was initially designed. To address the issue of severe wire mesh obstruction within the cage housing, machine vision techniques were employed to repair the cage mesh and enhance images by using OpenCV. A dead duck recognition model was constructed based on Mask R-CNN, and further optimized with the Swin Transformer to overcome the limitation of Mask R-CNN’s global information integration. The accuracy of dead duck recognition among the SOLO v2, Mask R-CNN, and Mask R-CNN+Swin Transformer models was compared and analyzed. Experimental results demonstrated that under the condition of mAP value of 90%, the Mask R-CNN+Swin Transformer model achieved an overall dead duck recognition rate of 95.8% within the duck cages, outperforming other mainstream object detection algorithms on the autonomous inspection equipment.