In recent years, more and more traffic accidents have been caused by the illegal use of high beams. Therefore, the distinguishing of the vehicle headlight is vital for night driving and traffic supervision. Then, a method for distinguishing vehicle headlight based on data access of a thermal camera was proposed in this paper. There are two steps in this method. The first step is thermal image dynamic adjustment. In thermal image dynamic adjustment, the details of thermal images were enhanced by adjusting the temperature display dynamically and fusing features of multi-sequence images. The second step is vehicle headlight dynamic distinguishing, and features of vehicle headlight were extracted by YOLOv3. Then, the high beam and low beam were further distinguished by the filter based on the position and proportion relationship between the halo and the beam size of vehicle headlights. In addition, the accessed thermal image dataset during the night was used for training purposes. The results showed that the precision of this method was 94.2%, and the recall was 78.7% at a real-time speed of 9 frames per second (FPS). Compared with YOLOv3 on the Red Green Blue (RGB) image, the precision was further improved by 11.1%, and the recall was further improved by 5.1%. Dynamic adjustment and distinguishing method were also applied in single-shot multibox detector (SSD) network which has good performance in small-object detection. Compared with the SSD network on the RGB image, the precision was improved by 8.2% and the recall was improved by 4.6% when SSD network was improved by this method.

近年来，非法使用远光灯造成了越来越多的交通事故。因此，区分汽车大灯对于夜间驾驶和交通监管至关重要。然后，提出了一种基于热像仪数据访问的汽车前照灯识别方法。此方法有两个步骤。第一步是热图像动态调整。在热图像动态调整中，通过动态调整温度显示并融合多序列图像的功能来增强热图像的细节。第二步是车辆前灯动态识别，并通过YOLOv3提取车辆前灯的特征。然后，滤光器根据光环和车辆前照灯光束大小之间的位置和比例关系进一步区分远光和近光。另外，夜间访问的热图像数据集也用于训练目的。结果表明，该方法的精度为94.2％，在9帧/秒（FPS）的实时速度下的召回率为78.7％。与红绿蓝（RGB）图像上的YOLOv3相比，精度进一步提高了11.1％，召回率进一步提高了5.1％。动态调整与判别方法也被应用于单发多盒检测器（SSD）网络中，该网络在小目标检测中具有良好的性能。与RGB图像上的SSD网络相比，精度提高了8.2％，召回率提高了4。