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

IF 5.9 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Ziyu Lin;Yunfan Wu;Yuhang Ma;Junzhou Chen;Ronghui Zhang;Jiaming Wu;Guodong Yin;Liang Lin
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引用次数: 0

摘要

交通标志检测对于自动驾驶和高级驾驶辅助系统(ADASs)至关重要。然而,现有的方法难以解决低光条件下图像质量差和信息不足的挑战,导致检测精度下降,影响驾驶安全。为了解决这个问题,我们提出了专为低光环境设计的端到端实时交通标志检测算法YOLO-LLTS。YOLO-LLTS引入了三个主要贡献:与传统方法相比,用于小目标检测的高分辨率特征图(HRFM-SOD)模块保留了更多关于远处或微小交通标志的信息;多分支特征交互注意(MFIA)模块将特征与不同的感受野进行交互,提高信息利用率;先验引导特征增强(PGFE)模块通过改善亮度、边缘、对比度和补充详细信息来提高检测精度。此外,我们构建了一个新的数据集,中国夜间交通标志样本集(CNTSSS),涵盖了不同的夜间场景。实验表明,ylo - llts达到了最先进的性能,在TT100K-night上比以前的最佳方法性能高2.7% mAP50和1.6% mAP50:95,在CNTSSS上比以前的最佳方法性能高1.3% mAP50和1.9% mAP50:95,在GTSDB-night上比以前的最佳方法性能高7.5% mAP50和9.8% mAP50:95,在CCTSDB2021上性能更好。在边缘设备上的部署证实了其实时适用性和有效性。代码和数据集可从https://github.com/linzy88/YOLO-LLTS获得
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
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来源期刊
IEEE Transactions on Instrumentation and Measurement
IEEE Transactions on Instrumentation and Measurement 工程技术-工程:电子与电气
CiteScore
9.00
自引率
23.20%
发文量
1294
审稿时长
3.9 months
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.
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