GEI-YOLOv9-based leather defect detection algorithm research

To meet the demand for higher detection speed and detection accuracy in the current leather defect detection program. This paper proposes the GEI (GhostNetV2-EMA-InnerIoU-YOLOv9) leather defect detection model to solve the problems of lacking defection samples and improving detection speed and accuracy. Specifically, GhostNetV2 is used in the backbone of the YOLOv9 algorithm instead of the existing convolutional method. Then, the proposed model introduces the EMA attention module to improve the small target feature extraction capability. Finally, this paper uses Inner-IOU instead of CIOU to achieve faster and more efficient regression results. The experimental results show that the method proposed in this paper reduces the parameters by 9%, increases the FPS by 2.5% and achieves 94.7% mAP compared to YOLOv9.