TIR-YOLO-ADAS: A thermal infrared object detection framework for advanced driver assistance systems

Abstract

An object detection framework using thermal infrared (TIR) cameras is proposed to meet the needs of an advanced driver assistance system (ADAS) operating at night-time and in low-visibility conditions. The proposed detection framework, referred to as TIR-YOLO-ADAS, is an improvement of YOLOX for TIR object detection in ADAS. First, to address the disadvantages of TIR objects, the part of the attention mechanism is designed to enhance the discriminative ability of feature maps in the spatial and channel dimensions. Second, a focal loss function is used as the confidence loss function to enable the framework to focus on detection tasks of difficult, misclassified targets in the process of network training. The results of the ablation experiment on the Forward-looking infrared (FLIR) thermal ADAS dataset indicate that the proposed framework significantly improves the performance of TIR object detection. Comparative experimental results further show that TIR-YOLO-ADAS performs favourably when compared with three representative detection algorithms. To evaluate the practicality and feasibility of the proposed framework in various applications, a qualitative assessment in real road scenarios was conducted. The experimental results confirm that the proposed framework performs promisingly and could be integrated into vehicle platforms as an ADAS module.