Enhancing RODNet detection in complex road environments based on ESM and ISM methods

IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Yu Guo, Yaxin Xiao, Yan Zhou, Yanyan Li, Siyu Yang, Chuangrui Meng
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引用次数: 0

Abstract

In autonomous driving, accurately identifying traffic targets is crucial for ensuring the safe and reliable operation of autonomous vehicles. Millimeter-wave radar, known for its low cost, long detection range, and excellent performance under various weather conditions. Deep learning algorithms, particularly the radar object detection network (RODNet), have been effectively applied to radar target detection by analyzing the range-azimuth (RA) heatmaps that capture complex target features. However, the low angular resolution of radar RA heatmaps, combined with the high sensitivity of millimeter-wave radar to metal objects, makes adjacent targets prone to misdetection and increases the likelihood of misclassification of target types due to metal reflections from road obstacles. To address these issues, this paper proposes an innovative extension suppression method to enhance RA heatmaps, reducing interference between adjacent targets and significantly improving target resolution. Additionally, the paper incorporates Gaussian filtering, peak detection, and amplitude suppression algorithms to design an interference suppression method, accurately identifying and mitigating strong reflections from non-target regions, thereby improving detection efficiency in complex environments. The effectiveness and superiority of these methods have been fully validated, with AP improvements of 18% in overlapping scenarios, 2% in metal obstacle scenarios, and around 10% in high-speed scenarios compared to the latest methods.
基于 ESM 和 ISM 方法加强复杂道路环境中的 RODNet 检测
在自动驾驶中,准确识别交通目标是确保自动驾驶汽车安全可靠运行的关键。毫米波雷达以其成本低、探测距离远、在各种天气条件下性能优异而著称。深度学习算法,特别是雷达目标检测网络(RODNet),通过分析捕捉复杂目标特征的测距方位(RA)热图,已被有效地应用于雷达目标检测。然而,雷达方位角热图的角度分辨率较低,再加上毫米波雷达对金属物体的高灵敏度,使得相邻目标容易被误检,并增加了因道路障碍物的金属反射而导致目标类型分类错误的可能性。为解决这些问题,本文提出了一种创新的扩展抑制方法来增强 RA 热图,减少相邻目标之间的干扰,并显著提高目标分辨率。此外,本文还结合高斯滤波、峰值检测和振幅抑制算法设计了一种干扰抑制方法,可准确识别和减轻来自非目标区域的强反射,从而提高复杂环境下的探测效率。这些方法的有效性和优越性已得到充分验证,与最新方法相比,在重叠场景中 AP 提高了 18%,在金属障碍物场景中提高了 2%,在高速场景中提高了约 10%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
期刊介绍: Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,
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