Straw Defect Detection Algorithm Based on Pruned YOLOv3

Abstract

To solve the problem of defect detection in straw pipeline production, this paper proposes an efficient and fast straw defect detection algorithm (IPOY) based on pruned YOLOv3. Algorithm adopts YOLOv3 model, and then trains the model with channel sparsity regularization, prunes channels with small scaling factors after sparse training, finally fine-tune the pruned network. This process was iterated several times to compress the YOLOv3 model to achieve a lighter model volume, reduce the computational cost of the model, and make the model suitable for industrial production to facilitate application migration to mobile devices. Experimental results show that the proposed algorithm can compress the volume of YOLOv3 model to the maximum extent and maintain the high precision of detection.