标题： VR-YOLO：基于YOLO的视角鲁棒性增强PCB缺陷检测 显示英文标题

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

摘要： 大规模电路和系统的集成强调了电子元件自动缺陷检测的重要性。 YOLO图像检测模型已被用于检测PCB缺陷，它已成为传统工业生产中的典型人工智能辅助案例。 然而，传统的检测算法对目标图像的角度、方向和清晰度有严格的要求。 本文提出了一种基于YOLOv8模型的增强型PCB缺陷检测算法，命名为VR-YOLO。 该算法旨在提高模型的泛化性能，并在实际应用场景中增强视角鲁棒性。 我们首先通过扩展PCB缺陷数据集，结合多种场景并分割样本，提出了一种多样化的场景增强（DSE）方法，以提高目标多样性。 然后，通过考虑角度损失并引入额外的注意力机制，提出了一种新颖的关键对象聚焦（KOF）方案，以增强小目标特征的细粒度学习。 实验结果表明，我们的改进型PCB缺陷检测方法在原始测试图像上的平均精度（mAP）达到了98.9%，在视角偏移的测试图像（水平和垂直剪切系数为$\pm 0.06$，旋转角度为$\pm 10$度）上的mAP达到了94.7%，与基线YOLO模型相比有显著提升，且计算成本几乎可以忽略不计。 显示英文摘要

摘要： 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.