红外热成像技术在ADAS系统中的应用

针对高级驾驶辅助系统(ADAS)要求的驾驶安全与现有的汽车传感器套件无法充分检测汽车或行人的矛盾，研究红外热成像技术在ADAS系统中的应用。首先讨论了在典型的汽车传感器套件中添加红外热像仪的必要性，然后对图像进行预处理，同时结合深度学习下的目标检测YOLOv5算法进行模型训练，最后用实验数据证明该算法能在复杂的驾驶环境中可以更好地检测和分类交通目标，从而帮助ADAS系统实现兼顾精度与实时性的目标检测。

Application of infrared thermal imaging technology in ADAS system

In this paper,the application of infrared thermal imaging technology in the ADAS system is studied,aiming at the contradiction between driving safety required by the advanced driver assistance system (ADAS) and that existing vehicle sensor kits cannot adequately detect vehicles or pedestrians.First,the necessity of adding an infrared camera to a typical car sensor kit is discussed,and then preprocessing the images while performing model training combined with the YOLOv5 algorithm for target detection under deep learning.Finally the experimental data are used to prove that the algorithm can better detect and classify traffic targets in a complex driving environment,thus helping the ADAS system to achieve target detection with both accuracy and real time.