{"title":"一种用于低光和小物体检测的改进YOLOX方法:隧道施工现场PPE","authors":"Zijian Wang, Zixiang Cai, Yimin Wu","doi":"10.1093/jcde/qwad042","DOIUrl":null,"url":null,"abstract":"\n Tunnel construction sites pose a significant safety risk to workers due to the low-light conditions that can affect visibility and lead to accidents. Therefore, identifying personal protective equipment (PPE) is critical to prevent injuries and fatalities. A few research has addressed the challenges posed by tunnel construction sites whose light conditions are lower and images are captured from a distance. In this study, we proposed an improved YOLOX approach and a new dataset for detecting low-light and small PPE. We modified the YOLOX architecture by adding ConvNeXt modules to the backbone for deep feature extraction and introducing the fourth YOLOX head for enhancing multiscale prediction. Additionally, we adopted the CLAHE algorithm for augmenting low-light images after comparing it with eight other methods. Consequently, the improved YOLOX approach achieves an mAP of 86.94%, which is 4.23% higher than the original model and outperforms selected state-of-the-art. It also improves the AP of small object classes by 7.17% on average and attains a real-time processing speed of 22 FPS. Furthermore, we constructed a novel dataset with 8,285 low-light instances and 6,814 small ones. The improved YOLOX approach offers accurate and efficient detection performance, which can reduce safety incidents on tunnel construction sites.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.8000,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An improved YOLOX approach for low-light and small object detection: PPE on tunnel construction sites\",\"authors\":\"Zijian Wang, Zixiang Cai, Yimin Wu\",\"doi\":\"10.1093/jcde/qwad042\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Tunnel construction sites pose a significant safety risk to workers due to the low-light conditions that can affect visibility and lead to accidents. Therefore, identifying personal protective equipment (PPE) is critical to prevent injuries and fatalities. A few research has addressed the challenges posed by tunnel construction sites whose light conditions are lower and images are captured from a distance. In this study, we proposed an improved YOLOX approach and a new dataset for detecting low-light and small PPE. We modified the YOLOX architecture by adding ConvNeXt modules to the backbone for deep feature extraction and introducing the fourth YOLOX head for enhancing multiscale prediction. Additionally, we adopted the CLAHE algorithm for augmenting low-light images after comparing it with eight other methods. Consequently, the improved YOLOX approach achieves an mAP of 86.94%, which is 4.23% higher than the original model and outperforms selected state-of-the-art. It also improves the AP of small object classes by 7.17% on average and attains a real-time processing speed of 22 FPS. Furthermore, we constructed a novel dataset with 8,285 low-light instances and 6,814 small ones. The improved YOLOX approach offers accurate and efficient detection performance, which can reduce safety incidents on tunnel construction sites.\",\"PeriodicalId\":48611,\"journal\":{\"name\":\"Journal of Computational Design and Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2023-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computational Design and Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1093/jcde/qwad042\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Design and Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1093/jcde/qwad042","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
An improved YOLOX approach for low-light and small object detection: PPE on tunnel construction sites
Tunnel construction sites pose a significant safety risk to workers due to the low-light conditions that can affect visibility and lead to accidents. Therefore, identifying personal protective equipment (PPE) is critical to prevent injuries and fatalities. A few research has addressed the challenges posed by tunnel construction sites whose light conditions are lower and images are captured from a distance. In this study, we proposed an improved YOLOX approach and a new dataset for detecting low-light and small PPE. We modified the YOLOX architecture by adding ConvNeXt modules to the backbone for deep feature extraction and introducing the fourth YOLOX head for enhancing multiscale prediction. Additionally, we adopted the CLAHE algorithm for augmenting low-light images after comparing it with eight other methods. Consequently, the improved YOLOX approach achieves an mAP of 86.94%, which is 4.23% higher than the original model and outperforms selected state-of-the-art. It also improves the AP of small object classes by 7.17% on average and attains a real-time processing speed of 22 FPS. Furthermore, we constructed a novel dataset with 8,285 low-light instances and 6,814 small ones. The improved YOLOX approach offers accurate and efficient detection performance, which can reduce safety incidents on tunnel construction sites.
期刊介绍:
Journal of Computational Design and Engineering is an international journal that aims to provide academia and industry with a venue for rapid publication of research papers reporting innovative computational methods and applications to achieve a major breakthrough, practical improvements, and bold new research directions within a wide range of design and engineering:
• Theory and its progress in computational advancement for design and engineering
• Development of computational framework to support large scale design and engineering
• Interaction issues among human, designed artifacts, and systems
• Knowledge-intensive technologies for intelligent and sustainable systems
• Emerging technology and convergence of technology fields presented with convincing design examples
• Educational issues for academia, practitioners, and future generation
• Proposal on new research directions as well as survey and retrospectives on mature field.