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

TL;DR

This paper introduces ESRPCB, a novel framework combining edge-guided super-resolution and ensemble learning to improve tiny PCB defect detection in low-resolution images, enhancing defect visibility and detection accuracy.

Contribution

The paper presents a new edge-guided super-resolution model with a ResCat structure and integrates ensemble learning for improved tiny PCB defect detection.

Findings

Enhanced defect detection accuracy in low-resolution PCB images.

Effective preservation of structural details through edge-guided super-resolution.

Improved detection performance demonstrated on PCB datasets.

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