ESRPCB: an Edge guided Super-Resolution model and Ensemble learning for tiny Printed Circuit Board Defect detection

• URL: http://arxiv.org/abs/2506.13476v1
• Date: Mon, 16 Jun 2025 13:34:35 GMT
• Title: ESRPCB: an Edge guided Super-Resolution model and Ensemble learning for tiny Printed Circuit Board Defect detection
• Authors: Xiem HoangVan, Dang Bui Dinh, Thanh Nguyen Canh, Van-Truong Nguyen,
• Abstract summary: This paper proposes a novel framework, named ESRPCB, which combines edgeguided super-resolution with ensemble learning to enhance PCBs defect detection.<n>By incorporating edge features, the super-resolution process preserves critical structural details, ensuring that tiny defects remain distinguishable in the enhanced image.
• Score: 0.33748750222488655
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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