Automated Defect Detection for Mass-Produced Electronic Components Based on YOLO Object Detection Models
Department of Electrical Engineering and Graduate School of Engineering Science and Technology（电气工程系和工程科学与技术研究生学院）；National Yunlin University of Science and Technology（云林科技大学）；Research Center for Information Technology Innovation（信息技术创新研究中心）；Academia Sinica（学术院）；Institute of Communications Engineering（通讯工程研究所）；National Sun Yat-sen University（国立中山大学）
Wei-Lung Mao, Chun-Chi Wang, Po-Heng Chou, Yen-Ting Liu
备注：
12 pages, 16 figures, 7 tables, and published in IEEE Sensors Journal
由于传统工业元器件的缺陷检测耗时耗力，给质检人员带来了沉重负担，也使得产品质量难以管控。本文针对工业中广泛应用的双列直插封装（dual in-line package，DIP），结合数码相机光学系统与深度学习（deep learning，DL）模型，提出了一套自动缺陷检测系统。研究针对DIP最常见的两类缺陷展开：（1）表面缺陷，（2）引脚缺陷。然而，缺陷元器件图像的匮乏给检测任务带来了挑战。为解决该问题，研究采用ConSinGAN生成规模合适的数据集用于训练与测试。研究探究了四种YOLO模型变体（v3、v4、v7和v9），分别测试了单独使用以及结合ConSinGAN数据增强的效果。所提出的结合ConSinGAN的YOLOv7模型在准确率（95.50%）、检测时间（285ms）上均优于其他YOLO版本，且性能远优于基于阈值的方法。此外，研究还开发了数据采集与监视控制系统（supervisory control and data acquisition，SCADA），并介绍了配套的传感器架构。所提出的自动缺陷检测方案可轻松适配多种缺陷类型或缺陷数据不足的场景。
英文摘要：
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.
CFIS-YOLO: A Lightweight Multi-Scale Fusion Network for Edge-Deployable Wood Defect Detection
School of Information and Engineering, Minzu University of China（中央民族大学信息工程学院）；School of Computer and Information Technology, Beijing Jiaotong University（北京交通大学计算机与信息技术学院）；School of Information and Communication Engineering, Beijing University of Posts and Telecommunications（北京邮电大学信息与通信工程学院）；Faculty of Science, The University of Hong Kong（香港大学理学院）
High-Performance Fine Defect Detection in Artificial Leather Using Dual Feature Pool Object Detection
Chongqing University of Posts and Telecommunications（重庆邮电大学）；Chongqing University（重庆大学）；Inspur Yunzhou Industrial Internet Co., Ltd（浪潮云洲工业互联网有限公司）；Guoqi Zhimo (Chongqing) Technology Co., Ltd.（国汽智磨（重庆）科技有限公司）；Shenzhen University（深圳大学）
Oxford Brookes University（牛津布鲁克斯大学）；Imperial College London（帝国理工学院）
Nowadays, there are more surgical procedures that are being performed using minimally invasive surgery (MIS). This is due to its many benefits, such as minimal post-operative problems, less bleeding, minor scarring, and a speedy recovery. However, the MIS's constrained field of view, small operating room, and indirect viewing of the operating scene could lead to surgical tools colliding and potentially harming human organs or tissues. Therefore, MIS problems can be considerably reduced, and surgical procedure accuracy and success rates can be increased by using an endoscopic video feed to dete…
School of Computer Science, Yangtze University（长江大学计算机科学学院）；School of Electronics & Information, Yangtze University（长江大学电子信息学院）；School of Urban Construction, Yangtze University（长江大学城市建设学院）
Steel pipes are widely used in high-risk and high-pressure scenarios such as oil, chemical, natural gas, shale gas, etc. If there is some defect in steel pipes, it will lead to serious adverse consequences. Applying object detection in the field of deep learning to pipe weld defect detection and identification can effectively improve inspection efficiency and promote the development of industrial automation. Most predecessors used traditional computer vision methods applied to detect defects of steel pipe weld seams. However, traditional computer vision methods rely on prior knowledge and can …
YOLO-Based Pipeline Monitoring in Challenging Visual Environments
Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa（电子电气与航海工程系（DITEN），热那亚大学）；Department of Bio-systems Engineering, Gyeongsang National University（生物系统工程系，全州国立大学）；Department of Mechanical Engineering and Maritime Studies, Western Norway University of Applied Sciences（机械工程与航海研究系，西挪威应用科学大学）
École de Technologie Supérieure（高等工程技术学院）；King Saud University（沙特国王大学）；Waseda University（早稻田大学）
Driven by the ever-increasing requirements of autonomous vehicles, such as traffic monitoring and driving assistant, deep learning-based object detection (DL-OD) has been increasingly attractive in intelligent transportation systems. However, it is difficult for the existing DL-OD schemes to realize the responsible, cost-saving, and energy-efficient autonomous vehicle systems due to low their inherent defects of low timeliness and high energy consumption. In this paper, we propose an object detection (OD) system based on edge-cloud cooperation and reconstructive convolutional neural networks, …
Detection of On-Ground Chestnuts Using Artificial Intelligence Toward Automated Picking
School of Automation Engineering, University of Electronic Science；Department of Biosystems \& Agricultural Engineering, Michigan State University, East Lansing, MI 48824, United States
Automated multi-class wound assessment using dedicated instance segmentation models for boundary detection and classification
Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology（密苏里科技大学机械与航空航天工程系）；Intelligent System Center, Missouri University of Science and Technology（密苏里科技大学智能系统中心）；Center for Biomedical Research, Missouri University of Science and Technology（密苏里科技大学生物医学研究中心）；Wound Care Department, Charles George Department of Veterans Affairs Medical Center（查尔斯·乔治退伍军人事务医疗中心伤口护理部）
Lidar based 3D object detection and classification tasks are essential for automated driving(AD). A Lidar sensor can provide the 3D point coud data reconstruction of the surrounding environment. But the detection in 3D point cloud still needs a strong algorithmic challenge. This paper consists of three parts.(1)Lidar-camera calib. (2)YOLO, based detection and PointCloud extraction, (3) k-means based point cloud segmentation. In our research, Camera can capture the image to make the Real-time 2D Object Detection by using YOLO, I transfer the bounding box to node whose function is making 3d obje…
Bialystok University of Technology（比亚韦斯托克理工大学）；Mammal Research Institute, Polish Academy of Sciences（波兰科学院哺乳动物研究所）
Camera traps are used worldwide to monitor wildlife. Despite the increasing availability of Deep Learning (DL) models, the effective usage of this technology to support wildlife monitoring is limited. This is mainly due to the complexity of DL technology and high computing requirements. This paper presents the implementation of the light-weight and state-of-the-art YOLOv5 architecture for automated labeling of camera trap images of mammals in the Bialowieza Forest (BF), Poland. The camera trapping data were organized and harmonized using TRAPPER software, an open source application for managin…
Profactor GmbH（Profactor有限公司）；JKU Institute of Computational Perception（约翰内斯·开普勒大学计算感知研究所）
Automated fiber placement (AFP) is an advanced manufacturing technology that increases the rate of production of composite materials. At the same time, the need for adaptable and fast inline control methods of such parts raises. Existing inspection systems make use of handcrafted filter chains and feature detectors, tuned for a specific measurement methods by domain experts. These methods hardly scale to new defects or different measurement devices. In this paper, we propose to formulate AFP defect detection as an image segmentation problem that can be solved in an end-to-end fashion using art…
Following the recent advances in deep networks, object detection and tracking algorithms with deep learning backbones have been improved significantly; however, this rapid development resulted in the necessity of large amounts of annotated labels. Even if the details of such semi-automatic annotation processes for most of these datasets are not known precisely, especially for the video annotations, some automated labeling processes are usually employed. Unfortunately, such approaches might result with erroneous annotations. In this work, different types of annotation errors for object detectio…
School of Science and Engineering, Chuo University（中央大学理工学部）；RITECS Inc.（RITECS公司）
This paper aims at constructing a light-weight object detector that inputs a depth and a color image from a stereo camera. Specifically, by extending the network architecture of YOLOv3 to 3D in the middle, it is possible to output in the depth direction. In addition, Intersection over Uninon (IoU) in 3D space is introduced to confirm the accuracy of region extraction results. In the field of deep learning, object detectors that use distance information as input are actively studied for utilizing automated driving. However, the conventional detector has a large network structure, and the real-t…
An Improved YOLOv8 Approach for Small Target Detection of Rice Spikelet Flowering in Field Environments
Shenzhen Institute of Modern Agricultural Equipment（深圳现代农业装备研究院）；Guangdong Institute of Modern Agricultural Equipment（广东现代农业装备研究院）；Key Laboratory of Modern Agricultural Intelligent Equipment in South China, Ministry of Agriculture and Rural Affairs（南方现代农业智能装备重点实验室，农业部）；Agro-Tech Extension Center of Guangdong Province（广东省农业技术推广中心）
A novel YOLO26-MoE optimized by an LLM agent for insulator fault detection considering UAV images
Department of Automation and Systems Engineering, Federal University of Santa Catarina, Florianópolis, Brazil（自动化与系统工程系，圣卡塔琳娜联邦大学，巴西弗洛里安波利斯）；Applications Lab, Faculty of Science, University of Salamanca, Plaza de los Caídos s/n, 37008 Salamanca, Spain（应用实验室，科学学院，萨拉曼卡大学，西班牙萨拉曼卡）
University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Oak Ridge National Laboratory（橡树岭国家实验室）；University of Oxford（牛津大学）；University of Michigan - Ann Arbor（密歇根大学安娜堡分校）
Videos captured using Transmission Electron Microscopy (TEM) can encode details regarding the morphological and temporal evolution of a material by taking snapshots of the microstructure sequentially. However, manual analysis of such video is tedious, error-prone, unreliable, and prohibitively time-consuming if one wishes to analyze a significant fraction of frames for even videos of modest length. In this work, we developed an automated TEM video analysis system for microstructural features based on the advanced object detection model called YOLO and tested the system on an in-situ ion irradi…
Electrical and Electronics Engineering Institute（电气与电子工程学院）；University of the Philippines Diliman（菲律宾大学迪利曼分校）
The inclusion of Computer Vision and Deep Learning technologies in Agriculture aims to increase the harvest quality, and productivity of farmers. During postharvest, the export market and quality evaluation are affected by assorting of fruits and vegetables. In particular, apples are susceptible to a wide range of defects that can occur during harvesting or/and during the post-harvesting period. This paper aims to help farmers with post-harvest handling by exploring if recent computer vision and deep learning methods such as the YOLOv3 (Redmon & Farhadi (2018)) can help in detecting healthy ap…
WoodYOLO: A Novel Object Detector for Wood Species Detection in Microscopic Images
Fraunhofer ITWM（弗劳恩霍夫信息技术与微系统研究所）；Thünen Institute of Wood Research（滕木材研究所）；Institute of Machine Learning and Analysis (IMLA)（机器学习与分析研究所）；Offenburg University（奥芬堡大学）
Comparative Analysis of Advanced AI-based Object Detection Models for Pavement Marking Quality Assessment during Daytime
PT Mitra Solusi Telematika；Ingram School of Engineering（英格拉姆工程学院）；Texas State University（德克萨斯州立大学）；Department of Civil Engineering（土木工程系）；Rowan University（罗文大学）；Department of Geography and Environmental Studies（地理与环境研究系）
Real-time object detection and robotic manipulation for agriculture using a YOLO-based learning approach
Imperial College（帝国理工学院）；University of Sheffield（谢菲尔德大学）；University of Manchester（曼彻斯特大学）；University of Southampton（南安普顿大学）；Fudan University（复旦大学）；Iowa State University（爱荷华州立大学）
Valeo Schalter und Sensoren GmbH（法雷奥开关与传感器有限公司）；Ilmenau University of Technology（伊尔默瑙工业大学）
Lidar based 3D object detection is inevitable for autonomous driving, because it directly links to environmental understanding and therefore builds the base for prediction and motion planning. The capacity of inferencing highly sparse 3D data in real-time is an ill-posed problem for lots of other application areas besides automated vehicles, e.g. augmented reality, personal robotics or industrial automation. We introduce Complex-YOLO, a state of the art real-time 3D object detection network on point clouds only. In this work, we describe a network that expands YOLOv2, a fast 2D standard object…
Huazhong University of Science and Technology（华中科技大学）；Hubei University of Technology（湖北工业大学）；Nanjing University（南京大学）
X-ray image plays an important role in manufacturing industry for quality assurance, because it can reflect the internal condition of weld region. However, the shape and scale of different defect types vary greatly, which makes it challenging for model to detect weld defects. In this paper, we propose a weld defect detection method based on convolution neural network, namely Lighter and Faster YOLO (LF-YOLO). In particularly, a reinforced multiscale feature (RMF) module is designed to implement both parameter-based and parameter-free multi-scale information extracting operation. RMF enables th…
Automating the product checkout process at conventional retail stores is a task poised to have large impacts on society generally speaking. Towards this end, reliable deep learning models that enable automated product counting for fast customer checkout can make this goal a reality. In this work, we propose a novel, region-based deep learning approach to automate product counting using a customized YOLOv5 object detection pipeline and the DeepSORT algorithm. Our results on challenging, real-world test videos demonstrate that our method can generalize its predictions to a sufficient level of ac…
Modern industry requires modern solutions for monitoring the automatic production of goods. Smart monitoring of the functionality of the mechanical parts of technology systems or machines is mandatory for a fully automatic production process. Although Deep Learning has been advancing, allowing for real-time object detection and other tasks, little has been investigated about the effectiveness of specially designed Convolutional Neural Networks for defect detection and industrial object recognition. In the particular study, we employed six publically available industrial-related datasets contai…
