YOLO-Based Defect Detection for Metal Sheets

2025/09/30 by Chou, Po-Heng, Wang, Chun-Chi, Mao, Wei-Lung
#68T07 #68T45 #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #I.2.10 #I.4.7 #I.5.4 #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2509.25659

Abstract

In this paper, we propose a YOLO-based deep learning (DL) model for automatic defect detection to solve the time-consuming and labor-intensive tasks in industrial manufacturing. In our experiments, the images of metal sheets are used as the dataset for training the YOLO model to detect the defects on the surfaces and in the holes of metal sheets. However, the lack of metal sheet images significantly degrades the performance of detection accuracy. To address this issue, the ConSinGAN is used to generate a considerable amount of data. Four versions of the YOLO model (i.e., YOLOv3, v4, v7, and v9) are combined with the ConSinGAN for data augmentation. The proposed YOLOv9 model with ConSinGAN outperforms the other YOLO models with an accuracy of 91.3%, and a detection time of 146 ms. The proposed YOLOv9 model is integrated into manufacturing hardware and a supervisory control and data acquisition (SCADA) system to establish a practical automated optical inspection (AOI) system. Additionally, the proposed automated defect detection is easily applied to other components in industrial manufacturing.

Citations

• NGGAN: Noise Generation GAN Based on the Practical Measurement Dataset for Narrowband Powerline Communications
• Automated Defect Detection for Mass-Produced Electronic Components Based on YOLO Object Detection Models
• YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
• Real-Time Flying Object Detection with YOLOv8
• YOLOv6 v3.0: A Full-Scale Reloading
• YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
• YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors
• RepVGG: Making VGG-style ConvNets Great Again
• RepVGG: Making VGG-style ConvNets Great Again
• YOLOv4: Optimal Speed and Accuracy of Object Detection
• Improved Techniques for Training Single-Image GANs
• YOLOv3: An Incremental Improvement
• Path Aggregation Network for Instance Segmentation
• Improved Training of Wasserstein GANs
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• You Only Look Once: Unified, Real-Time Object Detection

Related

• Automated Defect Detection for Mass-Produced Electronic Components Based on YOLO Object Detection Models
• Detecting and Classifying Defective Products in Images Using YOLO
• Comparative Analysis of Object Detection Algorithms for Surface Defect Detection
• Deep Learning Strategies for Industrial Surface Defect Detection Systems
• Defect Detection in Photolithographic Patterns Using Deep Learning Models Trained on Synthetic Data