Brain-Inspired Interpretable Network Pruning for Smart Vision-Based Defect Detection Equipment

Abstract

Detection algorithms play an important role in the life-cycle management of smart vision-based defect detection equipment. This article proposes a brain-inspired interpretable network pruning method for smart detection equipment for online defect detection scenarios. A brain-inspired neuronal circuit decomposition model is constructed from the view of the structure physics of artificial neural networks. To meet the real-time requirements, an interpretable network pruning is proposed in three steps: First, a full-size basic convolutional neural network is constructed. Second, the convolutional neural circuit's extraction method based on a genetic algorithm is designed to evaluate the function of different neural units. Third, a pruning method is proposed to eliminate the redundant convolutional neural circuits and retain key units to balance the accuracy and time efficiency. The experimental results demonstrated the proposed pruning method can improve the frame-pre-second by 116% on the premise of maintaining the detection accuracy of 92%.