AUTHOR=Li Shixiao , Bai Pengfei , Qin Yuanfeng TITLE=Dynamic Adjustment and Distinguishing Method for Vehicle Headlight Based on Data Access of a Thermal Camera JOURNAL=Frontiers in Physics VOLUME=8 YEAR=2020 URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2020.00354 DOI=10.3389/fphy.2020.00354 ISSN=2296-424X ABSTRACT=

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.