YOLO-pdd：一种使用深度表示与序列图像的新型多尺度PCB缺陷检测方法
YOLO-pdd: A Novel Multi-scale PCB Defect Detection Method Using Deep Representations with Sequential Images
AI总结：
提出YOLO-pdd方法，结合YOLOv5与多尺度模块实现层级残差式连接，在大型PCB数据集上显著提升缺陷检测的精度、召回率和F1分数。
摘要：
随着PCB制造业的快速增长，对计算机视觉检测在生产过程中识别缺陷的需求日益增加。提高PCB缺陷检测模型的准确性和泛化能力仍然是一个重大挑战。本文提出了一种基于深度卷积神经网络（CNN）的高精度、鲁棒且实时的端到端PCB缺陷检测方法。传统方法通常存在精度低和适用性有限的问题。我们提出了一种结合YOLOv5和多尺度模块的新方法，用于层级残差式连接。在PCB缺陷检测中，噪声可能混淆背景和小目标。YOLOv5模型凭借其实时处理和精确目标检测能力提供了坚实的基础。多尺度模块通过在单个块内引入层级残差式连接扩展了传统方法，实现了多尺度特征提取。这种即插即用模块通过提取多尺度、多层次的特征显著提升了性能，这些特征有助于识别不同大小和复杂度的缺陷。我们的多尺度架构将特征提取、缺陷定位和分类集成到一个统一的网络中。在大型PCB数据集上的实验表明，与现有方法相比，精度、召回率和F1分数均有显著提升。这项工作推进了PCB缺陷检测的计算机视觉检测，为PCB制造业中高精度、鲁棒、实时和领域自适应的缺陷检测提供了可靠的解决方案。
英文摘要：
With the rapid growth of the PCB manufacturing industry, there is an increasing demand for computer vision inspection to detect defects during production. Improving the accuracy and generalization of PCB defect detection models remains a significant challenge. This paper proposes a high-precision, robust, and real-time end-to-end method for PCB defect detection based on deep Convolutional Neural Networks (CNN). Traditional methods often suffer from low accuracy and limited applicability. We propose a novel approach combining YOLOv5 and multiscale modules for hierarchical residual-like connections. In PCB defect detection, noise can confuse the background and small targets. The YOLOv5 model provides a strong foundation with its real-time processing and accurate object detection capabilities. The multi-scale module extends traditional approaches by incorporating hierarchical residual-like connections within a single block, enabling multiscale feature extraction. This plug-and-play module significantly enhances performance by extracting features at multiple scales and levels, which are useful for identifying defects of varying sizes and complexities. Our multi-scale architecture integrates feature extraction, defect localization, and classification into a unified network. Experiments on a large-scale PCB dataset demonstrate significant improvements in precision, recall, and F1-score compared to existing methods. This work advances computer vision inspection for PCB defect detection, providing a reliable solution for high-precision, robust, real-time, and domain-adaptive defect detection in the PCB manufacturing industry.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
