pcb-defect-detection-using-yolov8x
PCB- Defect Detection using YOLOv8x a Research Project
https://github.com/gokulprasanth-m/pcb-defect-detection-using-yolov8x
Science Score: 44.0%
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Repository
PCB- Defect Detection using YOLOv8x a Research Project
Basic Info
- Host: GitHub
- Owner: GOKULPRASANTH-M
- Default Branch: main
- Size: 543 KB
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Metadata Files
README.md
PCB-Defect-Detection-using-YOLOv8x
PCB- Defect Detection using YOLOv8x a Research Project In this project, we designed and implemented YOLOv8x-HICAUps, a cutting-edge deep learning model aimed at automating the detection of surface defects in Printed Circuit Boards (PCBs) with high precision and efficiency. The model introduces several key innovations:
HorNet Backbone: Enhances feature extraction from high-density PCB layouts by capturing intricate textures and micro-patterns.
CBAM Attention Module: Improves focus on defect-relevant regions by combining spatial and channel attention, leading to more accurate detection of subtle anomalies like mouse bites, spurs, and pinholes.
Attention-Based Upsampling: Retains fine-grained details during the decoding phase to avoid blurring of small defects and ensures better boundary preservation.
Lightweight Detection Head: Tailored for real-time inference on embedded hardware and edge devices, balancing speed and accuracy.
The model was extensively evaluated on standard PCB defect datasets and achieved a detection accuracy of 98.3%, outperforming state-of-the-art approaches. Designed with deployment in mind, YOLOv8x-HICAUps bridges the gap between academic research and practical manufacturing applications, offering a scalable, low-complexity, and high-performance solution for smart electronics inspection.
Owner
- Login: GOKULPRASANTH-M
- Kind: user
- Repositories: 1
- Profile: https://github.com/GOKULPRASANTH-M
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this work, please cite it as below:"
authors:
- family-names: Mounika
given-names: M
- family-names: Gokulprasanth
given-names: M
- family-names: Vadivu
given-names: G
title: "YOLOV8X-HICAUPS: An Attention Based Approach for Accurate PCB Defect Detection"
version: "1.0"
doi: 10.33564/ijeast.2025.v09i11.013
date-released: 2025-06-15
type: article
journal: "International Journal of Engineering Applied Sciences and Technology"
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