An EA-based pruning on improved YOLOv3 for rapid copper elbow surface defect detection

Abstract

Even though deep learning models can be applied for the surface defect detection of industry products, it remains a challenge to widely deploy them on embedded carriers due to high computation and large memory requirements. To deal with this challenge, this paper proposed to search for the best pruning architecture of the YOLO(You Only Look Once)v3 model with the Particle Swarm Optimization evolutionary algorithm for model channel Pruning (PSOP), in addition to focusing on the importance of filters in a convolutional layer. An automatic sparsity training technique is induced in PSOP to generate an initial model before pruning. PSO searching speed is accelerated as a fast evaluation method is employed during training. A linear multi-object strategy is utilized to help PSO find the best structure for the pruning model in the searching period. PSOP outperforms many outstanding pruning methods. For instance, evaluated by our copper elbow dataset, the pruned YOLOv3 saves 62.93% of the model volume, 72.28% floating-point operations, and 62.96% parameter size with only 3.13% accuracy loss. The pruned YOLOv3 model is successfully deployed on NVIDIA Jetson Nano B01 embedded platform to improve copper elbow plant productivity.