Ziyu Lin;Yunfan Wu;Yuhang Ma;Junzhou Chen;Ronghui Zhang;Jiaming Wu;Guodong Yin;Liang Lin
{"title":"基于先验引导增强和多分支特征交互的实时弱光交通标志检测","authors":"Ziyu Lin;Yunfan Wu;Yuhang Ma;Junzhou Chen;Ronghui Zhang;Jiaming Wu;Guodong Yin;Liang Lin","doi":"10.1109/TIM.2025.3604925","DOIUrl":null,"url":null,"abstract":"Traffic sign detection is essential for autonomous driving and advanced driver assistance systems (ADASs). However, existing methods struggle to address the challenges of poor image quality and insufficient information under low-light conditions, leading to a decline in detection accuracy and affecting driving safety. To address this issue, we propose YOLO-LLTS, an end-to-end real-time traffic sign detection algorithm specifically designed for low-light environments. YOLO-LLTS introduces three main contributions: the high-resolution feature map for small object detection (HRFM-SOD) module retains more information about distant or tiny traffic signs compared to traditional methods; the multibranch feature interaction attention (MFIA) module interacts features with different receptive fields to improve information utilization; the prior-guided feature enhancement (PGFE) module enhances detection accuracy by improving brightness, edges, contrast, and supplementing detailed information. Additionally, we construct a new dataset, Chinese Nighttime Traffic Sign Sample Set (CNTSSS), covering diverse nighttime scenarios. Experiments show that YOLO-LLTS achieves state-of-the-art performance, outperforming previous best methods by 2.7% mAP50 and 1.6% mAP50:95 on TT100K-night, 1.3% mAP50 and 1.9% mAP50:95 on CNTSSS, 7.5% mAP50 and 9.8% mAP50:95 on GTSDB-night, and superior results on CCTSDB2021. Deployment on edge devices confirms its real-time applicability and effectiveness. The code and the dataset are available at <uri>https://github.com/linzy88/YOLO-LLTS</uri>","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-18"},"PeriodicalIF":5.9000,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"YOLO-LLTS: Real-Time Low-Light Traffic Sign Detection via Prior-Guided Enhancement and Multibranch Feature Interaction\",\"authors\":\"Ziyu Lin;Yunfan Wu;Yuhang Ma;Junzhou Chen;Ronghui Zhang;Jiaming Wu;Guodong Yin;Liang Lin\",\"doi\":\"10.1109/TIM.2025.3604925\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traffic sign detection is essential for autonomous driving and advanced driver assistance systems (ADASs). However, existing methods struggle to address the challenges of poor image quality and insufficient information under low-light conditions, leading to a decline in detection accuracy and affecting driving safety. To address this issue, we propose YOLO-LLTS, an end-to-end real-time traffic sign detection algorithm specifically designed for low-light environments. YOLO-LLTS introduces three main contributions: the high-resolution feature map for small object detection (HRFM-SOD) module retains more information about distant or tiny traffic signs compared to traditional methods; the multibranch feature interaction attention (MFIA) module interacts features with different receptive fields to improve information utilization; the prior-guided feature enhancement (PGFE) module enhances detection accuracy by improving brightness, edges, contrast, and supplementing detailed information. Additionally, we construct a new dataset, Chinese Nighttime Traffic Sign Sample Set (CNTSSS), covering diverse nighttime scenarios. Experiments show that YOLO-LLTS achieves state-of-the-art performance, outperforming previous best methods by 2.7% mAP50 and 1.6% mAP50:95 on TT100K-night, 1.3% mAP50 and 1.9% mAP50:95 on CNTSSS, 7.5% mAP50 and 9.8% mAP50:95 on GTSDB-night, and superior results on CCTSDB2021. Deployment on edge devices confirms its real-time applicability and effectiveness. 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YOLO-LLTS: Real-Time Low-Light Traffic Sign Detection via Prior-Guided Enhancement and Multibranch Feature Interaction
Traffic sign detection is essential for autonomous driving and advanced driver assistance systems (ADASs). However, existing methods struggle to address the challenges of poor image quality and insufficient information under low-light conditions, leading to a decline in detection accuracy and affecting driving safety. To address this issue, we propose YOLO-LLTS, an end-to-end real-time traffic sign detection algorithm specifically designed for low-light environments. YOLO-LLTS introduces three main contributions: the high-resolution feature map for small object detection (HRFM-SOD) module retains more information about distant or tiny traffic signs compared to traditional methods; the multibranch feature interaction attention (MFIA) module interacts features with different receptive fields to improve information utilization; the prior-guided feature enhancement (PGFE) module enhances detection accuracy by improving brightness, edges, contrast, and supplementing detailed information. Additionally, we construct a new dataset, Chinese Nighttime Traffic Sign Sample Set (CNTSSS), covering diverse nighttime scenarios. Experiments show that YOLO-LLTS achieves state-of-the-art performance, outperforming previous best methods by 2.7% mAP50 and 1.6% mAP50:95 on TT100K-night, 1.3% mAP50 and 1.9% mAP50:95 on CNTSSS, 7.5% mAP50 and 9.8% mAP50:95 on GTSDB-night, and superior results on CCTSDB2021. Deployment on edge devices confirms its real-time applicability and effectiveness. The code and the dataset are available at https://github.com/linzy88/YOLO-LLTS
期刊介绍:
Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.