提升PCB缺陷检测在视角变化下的稳定性，精度不降反升。
VR-YOLO: Enhancing PCB Defect Detection with Viewpoint Robustness Based on YOLO
- 通过多样化场景增强数据集，提升目标多样性。
- 引入关键对象聚焦机制，强化小目标特征学习能力。
- 在视角偏移下仍保持94.7%精度，适合工业实际应用。
大规模电路系统集成凸显了电子元件自动化缺陷检测的重要性。YOLO图像检测模型已被用于PCB缺陷检测，成为传统工业生产中典型的AI辅助案例。然而，传统检测算法对目标图像的角度、方向和清晰度要求严格。本文提出基于YOLOv8的增强型PCB缺陷检测算法VR-YOLO，旨在提升模型泛化能力并增强实际应用中的视角鲁棒性。首先，提出多样化场景增强（DSE）方法，通过引入多样场景并分割样本以提升目标多样性；随后，设计新型关键对象聚焦（KOF）方案，结合角度损失并引入额外注意力机制，增强小目标特征的细粒度学习。实验结果表明，该改进方法在原始测试图像上达到98.9%的平均精度（mAP），在视角偏移（水平与垂直剪切系数±0.06，旋转角±10度）测试图像上仍达94.7%，显著优于基线YOLO模型，且计算开销几乎无增加。
原文摘要 · Abstract (English)
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.
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