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New Thermal Automotive Dataset for Object Detection

—Although there are many efﬁcient deep learning methods, object detection and classiﬁcation in visible spectrum have many limitations especially in case of poor light conditions. To ﬁll this gap, we created a novel thermal video database containing few thousands of frames with annotated objects acquired in far infrared thermal spectrum. Thanks to this we were able to show its usability in the trafﬁc object recognition based on the YOLOv5 network, properly trained to gain maximal performance on thermal images, which contain many small objects and are characteristic of different properties than the visible spectrum counterparts. The proposed thermal database, as well as the fully trained model are main contributions of this paper. These are made available free for other researchers. Additionally, based on the highly efﬁcient car detector we show its application in the car speed measurement based exclusively on thermal images. The proposed system can be also used in the Advanced Driver-Assistance Systems (ADAS), and help autonomous driving.