Target Detection Algorithm Based on MMW Radar and Camera Fusion

Shouyi Lu, Z. Bao, Yongshuai Zhi, Sumin Zhang
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引用次数: 2

Abstract

With the increasing demand of consumers for car safety and comfort, intelligent driving technology has received extensive attention and research. A robust and reliable vehicle detection and tracking system is one of the key modules for intelligent vehicles to perceive the surrounding environment. At present, the mass-produced intelligent driving system mainly uses millimeter-wave radar and monocular camera to detect and track vehicles. Despite their advantages, the drawbacks of these two sensors make them insufficient when used separately. Therefore, in order to improve the target detection ability of the system, the research on multi-sensor information fusion is particularly important. This paper presents an information fusion approach based on a millimeter-wave radar and a monocular camera. Firstly, the millimeter-wave radar and monocular camera are used for target detection separately. Then the target data is extracted and recognized. Finally, the same target data detected by the two sensors are fused at the decision level. The proposed algorithm is tested on typical urban roads and urban expressways. Experiments show that the proposed algorithm can effectively improve the stability and reliability of target recognition and reduce the probability of missing target detection.
基于毫米波雷达与相机融合的目标检测算法
随着消费者对汽车安全性和舒适性需求的不断提高,智能驾驶技术得到了广泛的关注和研究。鲁棒可靠的车辆检测与跟踪系统是智能车辆感知周围环境的关键模块之一。目前量产的智能驾驶系统主要采用毫米波雷达和单目摄像头对车辆进行探测和跟踪。尽管这两种传感器有各自的优点,但它们的缺点使它们单独使用时不够充分。因此,为了提高系统的目标检测能力,多传感器信息融合的研究就显得尤为重要。提出了一种基于毫米波雷达和单目摄像机的信息融合方法。首先,采用毫米波雷达和单目摄像机分别进行目标检测;然后对目标数据进行提取和识别。最后,在决策层对两个传感器检测到的相同目标数据进行融合。在典型城市道路和城市高速公路上进行了试验。实验表明,该算法能有效提高目标识别的稳定性和可靠性,降低检测到缺失目标的概率。
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