Multi-Scale PCB Defect Detection with YOLOv8 Network Improved via Pruning and Lightweight Network

TL;DR

This paper presents a multi-scale PCB defect detection method based on YOLOv8, optimized with lightweight structures, adaptive pruning, and specialized loss functions to enhance accuracy and speed for tiny defect detection.

Contribution

It introduces a novel lightweight YOLOv8-based model with multi-level feature fusion, a new detection head, and an adaptive pruning strategy for improved PCB defect detection.

Findings

Achieved 99.32% mAP0.5 on PCB dataset

Improved detection speed and accuracy over YOLOv8n

Reduced model complexity with pruning and lightweight design

Abstract

With the high density of printed circuit board (PCB) design and the high speed of production, the traditional PCB defect detection model is difficult to take into account the accuracy and computational cost, and cannot meet the requirements of high accuracy and real-time detection of tiny defects. Therefore, in this paper, a multi-scale PCB defect detection method is improved with YOLOv8 using a comprehensive strategy of tiny target sensitivity strategy, network lightweighting and adaptive pruning, which is able to improve the detection speed and accuracy by optimizing the backbone network, the neck network and the detection head, the loss function and the adaptive pruning rate. Firstly, a Ghost-HGNetv2 structure with fewer parameters is used in the backbone network, and multilevel features are used to extract image semantic features to discover accurate defects. Secondly, we integrate C2f-Faster with small number of parameters in the neck section to enhance the ability of multi-level feature fusion. Next, in the Head part, we design a new GCDetect detection head, which allows the prediction of bounding boxes and categories to share the weights of GroupConv, and uses a small number of grouping convolutions to accomplish the regression and classification tasks, which significantly reduces the number of parameters while maintaining the accuracy of detection. We also design the Inner-MPDIoU boundary loss function to improve the detection and localization of tiny targets. Finally, the model was pruned by an optimized adaptive pruning rate to further reduce the complexity of the model. Experimental results show that the model exhibits advantages in terms of accuracy and speed. On the publicly available PCB defect dataset, mAP0.5 reaches 99.32% and mAP0.5:0.9 reaches 75.18%, which is 10.13% higher compared to YOLOv8n.