Duck Down Recognition and Classification Based on YOLOv8 Improved by GSconv and CBAM

The composition of duck down is complex and difficult to separate, so at present, in the process of composition detection of duck down, the composition of duck down are recognized by the method of artificial separation, which is inefficient and subjective. In this paper, a duck down recognition method based on the improved YOLOv8 algorithm is proposed, which realizes image enhancement through image preprocessing and reduces the influence of complex background. By improving the YOLOv8 algorithm, the accurate identification of duck down was realized. The lightweight modules GSconv and VoV-GSCSP are used to increase the speed of model detection and improve the ability to detect multiple targets. Add the CBAM module to improve the feature extraction ability and detection accuracy, and improve the model's ability to detect small targets. The experimental shows that the average accuracy of the improved model for the identification of duck down (MAP50 and MAP50-95) is 99.1% and 68.1%, respectively. By comparing the mainstream target detection networks, it is demonstrated that the improved YOLOv8 has the highest detection accuracy and the fastest detection speed, which makes it practical and reliable for detection of down and similar small and multi-target targets in practical applications.