Automated Defect Detection for Mass-Produced Electronic Components Based on YOLO Object Detection Models

• URL: http://arxiv.org/abs/2510.01914v2
• Date: Sat, 04 Oct 2025 03:11:42 GMT
• Title: Automated Defect Detection for Mass-Produced Electronic Components Based on YOLO Object Detection Models
• Authors: Wei-Lung Mao, Chun-Chi Wang, Po-Heng Chou, Yen-Ting Liu,
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Since the defect detection of conventional industry components is time-consuming and labor-intensive, it leads to a significant burden on quality inspection personnel and makes it difficult to manage product quality. In this paper, we propose an automated defect detection system for the dual in-line package (DIP) that is widely used in industry, using digital camera optics and a deep learning (DL)-based model. The two most common defect categories of DIP are examined: (1) surface defects, and (2) pin-leg defects. However, the lack of defective component images leads to a challenge for detection tasks. To solve this problem, the ConSinGAN is used to generate a suitable-sized dataset for training and testing. Four varieties of the YOLO model are investigated (v3, v4, v7, and v9), both in isolation and with the ConSinGAN augmentation. 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. In addition, the supervisory control and data acquisition (SCADA) system is developed, and the associated sensor architecture is described. The proposed automated defect detection can be easily established with numerous types of defects or insufficient defect data.

Related papers

• Automated Neural Architecture Design for Industrial Defect Detection [40.72225113019191]
  Industrial surface defect detection (SDD) is critical for ensuring product quality and manufacturing reliability.<n>We propose AutoNAD, an automated neural architecture design framework for SDD that jointly searches over convolutions, transformers, and multi-layer perceptrons.<n>The effectiveness of AutoNAD is validated on three industrial defect datasets and further applied within a defect imaging and detection platform.
  arXiv  Detail & Related papers  (2025-10-08T05:37:59Z)
• YOLO-Based Defect Detection for Metal Sheets [0.0]
  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.
  arXiv  Detail & Related papers  (2025-09-30T01:56:44Z)
• AI-Driven Multi-Stage Computer Vision System for Defect Detection in Laser-Engraved Industrial Nameplates [0.0]
  This paper presents a proof of concept for an AI-driven computer vision system that inspects and verifies laser-engraved nameplates.<n>The system achieves 91.33% accuracy and 100% recall, ensuring that defective nameplates are consistently detected and addressed.
  arXiv  Detail & Related papers  (2025-03-05T11:19:17Z)
• Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing [0.0]
  One of the most promising use-cases for machine learning in industrial manufacturing is the early detection of defective products. We evaluate current vision transformer models together with anomaly detection methods. We give guidelines for choosing a suitable model architecture for a quality control system in practice.
  arXiv  Detail & Related papers  (2024-11-22T14:12:35Z)
• Uncertainty Estimation for 3D Object Detection via Evidential Learning [63.61283174146648]
  We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections.
  arXiv  Detail & Related papers  (2024-10-31T13:13:32Z)
• AI-Powered Dynamic Fault Detection and Performance Assessment in Photovoltaic Systems [44.99833362998488]
  intermittent nature of photovoltaic (PV) solar energy leads to power losses of 10-70% and an average energy production decrease of 25%. Current fault detection strategies are costly and often yield unreliable results due to complex data signal profiles. This research presents a computational model using the PVlib library in Python, incorporating a dynamic loss quantification algorithm.
  arXiv  Detail & Related papers  (2024-08-19T23:52:06Z)
• Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [59.41026558455904]
  We focus on multi-modal anomaly detection. Specifically, we investigate early multi-modal approaches that attempted to utilize models pre-trained on large-scale visual datasets. We propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method to finetune the adaptors and learn task-oriented representation toward anomaly detection.
  arXiv  Detail & Related papers  (2024-01-06T07:30:41Z)
• YOLOv8 for Defect Inspection of Hexagonal Directed Self-Assembly Patterns: A Data-Centric Approach [6.142308190194335]
  We propose a method for obtaining coherent and complete labels for a dataset of hexagonal contact hole DSA patterns. We show that YOLOv8, a state-of-the-art neural network, achieves defect detection precisions of more than 0.9 mAP on our final dataset.
  arXiv  Detail & Related papers  (2023-07-28T12:17:01Z)
• Reference-based Defect Detection Network [57.89399576743665]
  The first issue is the texture shift which means a trained defect detector model will be easily affected by unseen texture. The second issue is partial visual confusion which indicates that a partial defect box is visually similar with a complete box. We propose a Reference-based Defect Detection Network (RDDN) to tackle these two problems.
  arXiv  Detail & Related papers  (2021-08-10T05:44:23Z)
• Computer Vision and Normalizing Flow Based Defect Detection [0.0]
  We present a two-stage defect detection network based on the object detection model YOLO, and the normalizing flow-based defect detection model DifferNet. Our model has high robustness and performance on defect detection using real-world video clips taken from a production line monitoring system. Our proposed model can learn on a small number of defect-free samples of single or multiple product types.
  arXiv  Detail & Related papers  (2020-12-12T05:38:21Z)
• SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection [63.253850875265115]
  Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples. We propose a modular acceleration system, called SUOD, to address it.
  arXiv  Detail & Related papers  (2020-03-11T00:22:50Z)

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.