VR-YOLO: Enhancing PCB Defect Detection with Viewpoint Robustness Based on YOLO

TL;DR

This paper introduces VR-YOLO, an improved PCB defect detection model based on YOLOv8, which enhances viewpoint robustness and generalization through data augmentation and attention mechanisms, achieving high accuracy even with viewpoint shifts.

Contribution

The paper presents VR-YOLO, a novel PCB defect detection algorithm that incorporates diversified scene enhancement and a key object focus scheme to improve viewpoint robustness and detection accuracy.

Findings

Achieves 98.9% mAP on original images.

Attains 94.7% mAP on images with viewpoint shifts.

Significantly outperforms baseline YOLO with minimal extra computation.

Abstract

The integration of large-scale circuits and systems emphasizes the importance of automated defect detection of electronic components. The YOLO image detection model has been used to detect PCB defects and it has become a typical AI-assisted case of traditional industrial production. However, conventional detection algorithms have stringent requirements for the angle, orientation, and clarity of target images. In this paper, we propose an enhanced PCB defect detection algorithm, named VR-YOLO, based on the YOLOv8 model. This algorithm aims to improve the model's generalization performance and enhance viewpoint robustness in practical application scenarios. We first propose a diversified scene enhancement (DSE) method by expanding the PCB defect dataset by incorporating diverse scenarios and segmenting samples to improve target diversity. A novel key object focus (KOF) scheme is then presented by considering angular loss and introducing an additional attention mechanism to enhance fine-grained learning of small target features. Experimental results demonstrate that our improved PCB defect detection approach achieves a mean average precision (mAP) of 98.9% for the original test images, and 94.7% for the test images with viewpoint shifts (horizontal and vertical shear coefficients of $\pm 0.06$ and rotation angle of $\pm 10$ degrees), showing significant improvements compared to the baseline YOLO model with negligible additional computational cost.