The intelligent vehicle target detection system can sense and recognize the surrounding pedestrians, vehicles and other objects through sensors, which is the basis for achieving intelligent vehicle unmanned driving. The laser imaging radar actively emits laser light and receives its reflected echo, which can form an angle-angle-distance-intensity image, making it easier to realize target recognition. The combination of the lidar and the infrared characteristics of the target can obtain more information and improve target recognition and anti-interference ability. In order to achieve fast and accurate moving target detection in a complex battlefield environment, this paper studies lidar imaging and target infrared features, as well as intelligent vehicle target detection, and proposes a target recognition method that combines target infrared features and lidar. Compensation makes it difficult to describe the disadvantages of moving targets in a single source data. The experimental results show that the laser and infrared fusion detection algorithm does not increase the complexity of the algorithm, which greatly improves the adaptability and robustness of the vehicle target detection algorithm, and improves the accuracy of the measurement detection algorithm.

智能车辆目标检测系统可以通过传感器感应和识别周围的行人，车辆和其他物体，这是实现智能车辆无人驾驶的基础。激光成像雷达主动发射激光并接收其反射的回波，可以形成角度-角度-距离-强度图像，从而更容易实现目标识别。激光雷达和目标的红外特性的结合可以获取更多信息，并提高目标识别和抗干扰能力。为了在复杂战场环境中实现快速准确的移动目标检测，本文研究了激光雷达成像和目标红外特征以及智能车辆目标检测，并提出了一种结合目标红外特征和激光雷达的目标识别方法。补偿使得难以在单个源数据中描述移动目标的缺点。实验结果表明，激光和红外融合检测算法没有增加算法的复杂度，大大提高了车辆目标检测算法的适应性和鲁棒性，提高了测量检测算法的准确性。