基于红外相机和毫米波雷达融合的烟雾遮挡无人驾驶车辆目标检测与跟踪

Object Detection and Tracking for Unmanned Vehicles Based on Fusion of Infrared Camera and MMW Radar in Smoke-obscured Environment

战场环境下无人驾驶车辆的感知系统易受烟雾、扬尘等天气的影响,对关键目标的检测与跟踪能力大大下降,造成目标漏检、目标误检、目标丢失等严重后果。针对该问题,开发毫米波雷达和红外相机融合系统,采用目标级融合方式建立简洁有效的融合规则,提炼和组合各传感器的优势信息,最终输出稳定的目标感知结果。对毫米波雷达的目标进行有效性检验和提取,并提出改进的基于密度的含噪声空间聚类应用算法,以减少毫米波雷达噪音干扰。以YOLOv4网络为基础,引入MobileNetv2主干网络,在网络训练过程中运用迁移学习方法,同时对红外数据样本进行扩充,解决了红外图像训练样本少的问题。试验结果表明,相较于仅基于红外相机的算法,融合检测算法在烟雾环境下的精度显著提升,且算法实时性高,实现了烟雾环境下毫米波雷达与红外相机融合的目标检测与跟踪,提高了无人驾驶车辆目标检测与跟踪系统的抗烟雾干扰能力。

In the battlefield environment,the perception system of unmanned vehicle is susceptible to the influence of weather such as smoke and dust.The ability to detect and track key objects is greatly reduced under harsh weather conditions,resulting in serious consequences,such as object miss-detection,object misdetection and object missing.To address this problem,a fusion system of MMW radar and infrared camera is developed.The object-level fusion method is adopted to establish simple and effective fusion rules,extract and combine the dominant information from each sensor,and finally output stable objective perception results.The objects of MMW radar are checked and extracted.And an improved DBSCAN clustering algorithm is proposed to reduce the noise of MMW radar.The MobileNetv2 backbone network is introduced based on the YOLOv4 network.In the process of network training,the transfer learning method is used to expand the infrared data samples,which solves the problem of fewer training samples of infrared images.The experimental results show that the fusion algorithm has significantly better accuracy and high real-time performance in the smoke environment compared with the algorithm based on infrared camera only,which realizes the object detection and tracking of the fusion of MMW radar and infrared camera in the smoke environment,and improves the anti-interference ability of the object detection and tracking system of unmanned vehicles.