YOLO-Based Defect Detection for Metal Sheets

• URL: http://arxiv.org/abs/2509.25659v1
• Date: Tue, 30 Sep 2025 01:56:44 GMT
• Title: YOLO-Based Defect Detection for Metal Sheets
• Authors: Po-Heng Chou, Chun-Chi Wang, Wei-Lung Mao,
• Abstract summary: We propose a YOLO-based deep learning (DL) model for automatic defect detection.<n>In our experiments, the images of metal sheets are used as the dataset for training the YOLO model.<n>The proposed YOLOv9 model with ConSinGAN outperforms the other YOLO models with an accuracy of 91.3%, and a detection time of 146 ms.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• YOLO-DS: Fine-Grained Feature Decoupling via Dual-Statistic Synergy Operator for Object Detection [55.58092342624062]
  We propose YOLO-DS, a framework built around a novel Dual-Statistic Synergy Operator (DSO)<n>YOLO-DS decouples object features by jointly modeling the channel-wise mean and the peak-to-mean difference.<n>On the MS-COCO benchmark, YOLO-DS consistently outperforms YOLOv8 across five model scales.
  arXiv  Detail & Related papers  (2026-01-26T05:50:32Z)
• YOLOA: Real-Time Affordance Detection via LLM Adapter [96.61111291833544]
  Affordance detection aims to jointly address the fundamental "what-where-how" challenge in embodied AI.<n>We introduce YOLO Affordance (YOLOA), a real-time affordance detection model that jointly handles object detection and affordance learning.<n>Experiments on our relabeled ADG-Det and IIT-Heat benchmarks demonstrate that YOLOA achieves state-of-the-art accuracy while maintaining real-time performance.
  arXiv  Detail & Related papers  (2025-12-03T03:53:31Z)
• Automated Defect Detection for Mass-Produced Electronic Components Based on YOLO Object Detection Models [0.0]
  We propose an automated defect detection system for the dual in-line package (DIP) that is widely used in industry.<n>The proposed YOLOv7 with ConSinGAN is superior to the other YOLO versions in accuracy of 95.50%, detection time of 285 ms, and is far superior to threshold-based approaches.
  arXiv  Detail & Related papers  (2025-10-02T11:33:16Z)
• Plastic Waste Classification Using Deep Learning: Insights from the WaDaBa Dataset [0.0]
  This study focuses on convolutional neural networks (CNNs) and object detection models like YOLO (You Only Look Once)<n>The study shows that YOLO- 11m achieved highest accuracy (98.03%) and mAP50 (0.990), with YOLO-11n performing similarly but highest mAP50(0.992)<n>Lightweight models like YOLO-10n trained faster but with lower accuracy, whereas MobileNet V2 showed impressive performance (97.12% accuracy) but fell short in object detection.
  arXiv  Detail & Related papers  (2024-12-28T18:00:52Z)
• HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection [12.865603495310328]
  HD-YOLO consists of Defect Aware Module (DAM) and Mixed Graph Network (MGNet) in the backbone.<n>HGANet combines hypergraph and attention mechanism to aggregate multi-scale features.<n>Cross-Scale Fusion (CSF) is proposed to adaptively fuse and handle features instead of simple concatenation and convolution.
  arXiv  Detail & Related papers  (2024-12-05T08:38:01Z)
• YOLO-ELA: Efficient Local Attention Modeling for High-Performance Real-Time Insulator Defect Detection [0.0]
  Existing detection methods for insulator defect identification from unmanned aerial vehicles struggle with complex background scenes and small objects. This paper proposes a new attention-based foundation architecture, YOLO-ELA, to address this issue. Experimental results on high-resolution UAV images show that our method achieved a state-of-the-art performance of 96.9% mAP0.5 and a real-time detection speed of 74.63 frames per second.
  arXiv  Detail & Related papers  (2024-10-15T16:00:01Z)
• Fall Detection for Industrial Setups Using YOLOv8 Variants [0.0]
  The YOLOv8m model, consisting of 25.9 million parameters and 79.1 GFLOPs, demonstrated a respectable balance between computational efficiency and detection performance. Although the YOLOv8l and YOLOv8x models presented higher precision and recall, their higher computational demands and model size make them less suitable for resource-constrained environments.
  arXiv  Detail & Related papers  (2024-08-08T17:24:54Z)
• YOLOv10: Real-Time End-to-End Object Detection [68.28699631793967]
  YOLOs have emerged as the predominant paradigm in the field of real-time object detection. The reliance on the non-maximum suppression (NMS) for post-processing hampers the end-to-end deployment of YOLOs. We introduce the holistic efficiency-accuracy driven model design strategy for YOLOs.
  arXiv  Detail & Related papers  (2024-05-23T11:44:29Z)
• YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object Detection [63.36722419180875]
  We provide an efficient and performant object detector, termed YOLO-MS.<n>We train our YOLO-MS on the MS COCO dataset from scratch without relying on any other large-scale datasets.<n>Our work can also serve as a plug-and-play module for other YOLO models.
  arXiv  Detail & Related papers  (2023-08-10T10:12:27Z)
• An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design [55.62660894625669]
  Lithography modeling is a crucial problem in chip design to ensure a chip design mask is manufacturable. Recent developments in machine learning have provided alternative solutions in replacing the time-consuming lithography simulations with deep neural networks. We propose a litho-aware data augmentation framework to resolve the dilemma of limited data and improve the machine learning model performance.
  arXiv  Detail & Related papers  (2022-10-27T20:53:39Z)
• A lightweight and accurate YOLO-like network for small target detection in Aerial Imagery [94.78943497436492]
  We present YOLO-S, a simple, fast and efficient network for small target detection. YOLO-S exploits a small feature extractor based on Darknet20, as well as skip connection, via both bypass and concatenation. YOLO-S has an 87% decrease of parameter size and almost one half FLOPs of YOLOv3, making practical the deployment for low-power industrial applications.
  arXiv  Detail & Related papers  (2022-04-05T16:29:49Z)
• LF-YOLO: A Lighter and Faster YOLO for Weld Defect Detection of X-ray Image [7.970559381165446]
  We propose a weld defect detection method based on convolution neural network (CNN), namely Lighter and Faster YOLO (LF-YOLO) To improve the performance of detection network, we propose an efficient feature extraction (EFE) module. Experimental results show that our weld defect network achieves satisfactory balance between performance and consumption, and reaches 92.9 mAP50 with 61.5 FPS.
  arXiv  Detail & Related papers  (2021-10-28T12:19:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.