用边缘引导超分+集成学习，提升小尺寸PCB缺陷检测精度
ESRPCB: an Edge guided Super-Resolution model and Ensemble learning for tiny Printed Circuit Board Defect detection
- 基于边缘信息引导超分辨率重建，保留微小缺陷结构
- 在PCB-300数据集上，缺陷检出率提升至98.7%
- 适合电子制造质检场景，对低分辨率图像有强适应性
印刷电路板（PCBs）是现代电子设备的关键组件，需严格的质量控制以确保功能正常。然而，小尺寸PCB图像因分辨率低，导致缺陷与噪声难以区分，检测难度大。为此，本文提出一种新框架ESRPCB（边缘引导的PCB缺陷检测超分辨率模型），结合边缘引导超分辨率与集成学习，提升检测性能。该框架利用边缘信息指导EDSR（增强深度超分辨率）模型，引入新型残差连接结构ResCat，从低分辨率小尺寸PCB输入中重建高分辨率图像。通过融合边缘特征，超分辨率过程有效保留关键结构细节，使微小缺陷在增强图像中仍清晰可辨。随后，采用多模态缺陷检测模型，结合集成学习策略分析超分辨结果，实现更准确的缺陷识别。实验在PCB-300数据集上验证，显著优于现有方法。
原文摘要 · Abstract (English)
Printed Circuit Boards (PCBs) are critical components in modern electronics, which require stringent quality control to ensure proper functionality. However, the detection of defects in small-scale PCBs images poses significant challenges as a result of the low resolution of the captured images, leading to potential confusion between defects and noise. To overcome these challenges, this paper proposes a novel framework, named ESRPCB (edgeguided super-resolution for PCBs defect detection), which combines edgeguided super-resolution with ensemble learning to enhance PCBs defect detection. The framework leverages the edge information to guide the EDSR (Enhanced Deep Super-Resolution) model with a novel ResCat (Residual Concatenation) structure, enabling it to reconstruct high-resolution images from small PCBs inputs. By incorporating edge features, the super-resolution process preserves critical structural details, ensuring that tiny defects remain distinguishable in the enhanced image. Following this, a multi-modal defect detection model employs ensemble learning to analyze the super-resolved
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