A complementary and precise vehicle detection approach in RGB-T images via semi-supervised transfer learning and decision-level fusion

Abstract

ABSTRACT Owing to the strong complementarity between visible and infra-red thermal (RGB-T) images, the two types of images receive the increased application of vehicle detection in traffic monitoring. Now, how to take full advantage of the two kinds of images for vehicle fusion detection has drawn wide publicity. Nevertheless, due to the requirement of high quality and high precision on vehicle fusion detection, and insufficiency of the infra-red thermal dataset, it is extremely hard to realize the vehicle fusion detection in RGB-T images. In our work, a complementary and precise vehicle fusion detection approach in RGB-T images is put forward, which combines the detection results of both visible and infra-red thermal images based on a decision-level fusion strategy. For vehicle detection in visible images, an effective and shallow network is constructed. For vehicle detection in infra-red thermal images, we do not directly train an effective network because of the insufficiency of the infra-red thermal vehicle samples. Instead, semi-supervised transfer learning is applied to combine the trained visible network for vehicle detection in infra-red thermal images. Next, for the decision-level fusion, we propose a novel fusion algorithm. It not only retains the vehicle results detected only in the visible images or only the infra-red thermal images but also fuses the detection results of the same vehicle in both visible and infra-red thermal images. Finally, VOT2019 and RGBT234 data sets are introduced to evaluate the proposed vehicle fusion detection approach. Compared with the main trend of current, it is indicated that the proposed approach can obtain competitive and superior vehicle fusion detection results in RGB-T images.