Rivera Velázquez Josué Manuel, Khoudour Louahdi, Saint Pierre Guillaume, Duthon Pierre, Liandrat Sébastien, Bernardin Frédéric, Fiss Sharon, Ivanov Igor, Peleg Raz
Cerema Occitanie, Research Team "Intelligent Transport Systems", 1 Avenue du Colonel Roche, 31400 Toulouse, France.
Cerema Centre-Est, Research Team "Intelligent Transport Systems", 8-10, Rue Bernard Palissy, 63017 Clermont-Ferrand, France.
J Imaging. 2022 Nov 9;8(11):306. doi: 10.3390/jimaging8110306.
Object detection is recognized as one of the most critical research areas for the perception of self-driving cars. Current vision systems combine visible imaging, LIDAR, and/or RADAR technology, allowing perception of the vehicle's surroundings. However, harsh weather conditions mitigate the performances of these systems. Under these circumstances, thermal imaging becomes the complementary solution to current systems not only because it makes it possible to detect and recognize the environment in the most extreme conditions, but also because thermal images are compatible with detection and recognition algorithms, such as those based on artificial neural networks. In this paper, an analysis of the resilience of thermal sensors in very unfavorable fog conditions is presented. The goal was to study the operational limits, i.e., the very degraded fog situation beyond which a thermal camera becomes unreliable. For the analysis, the mean pixel intensity and the contrast were used as indicators. Results showed that the angle of view (AOV) of a thermal camera is a determining parameter for object detection in foggy conditions. Additionally, results show that cameras with AOVs 18° and 30° are suitable for object detection, even under thick fog conditions (from 13 m meteorological optical range). These results were extended using object detection software, with which it is shown that, for the pedestrian, a detection rate ≥90% was achieved using the images from the 18° and 30° cameras.
目标检测被认为是自动驾驶汽车感知领域最关键的研究领域之一。当前的视觉系统结合了可见光成像、激光雷达和/或雷达技术，能够感知车辆周围环境。然而，恶劣天气条件会降低这些系统的性能。在这种情况下，热成像成为当前系统的补充解决方案，这不仅是因为它能够在最极端条件下检测和识别环境，还因为热图像与检测和识别算法兼容，比如基于人工神经网络的算法。本文对在非常不利的雾天条件下热传感器的弹性进行了分析。目标是研究其操作极限，即热成像相机变得不可靠的非常恶劣的雾天情况。在分析中，平均像素强度和对比度被用作指标。结果表明，热成像相机的视角（AOV）是雾天条件下目标检测的一个决定性参数。此外，结果表明，视角为18°和30°的相机即使在浓雾条件下（气象光学视程为13米）也适合目标检测。使用目标检测软件对这些结果进行了扩展，结果表明，对于行人，使用18°和30°相机拍摄的图像实现了≥90%的检测率。
