Abstract
Abstract. In this paper, we propose a method of object detection based on thermal images acquired from unmanned aerial vehicles (UAV). Compared with visible images, thermal images have lower requirements for illumination conditions, but they have some problems, such as blurred edges and low contrast. To address these problems, we propose to use the saliency map of thermal images for image enhancement as the attention mechanism of the object detector. In the paper, the YOLOv3 network is trained as a detection benchmark and BASNet is used to generate saliency maps from the thermal images. We fuse the thermal images with their corresponding saliency maps through the pixel-level weighted fusion method. Experiment results tested on real data have shown that the proposed method could realize the task of object detection in UAV-borne thermal images. The statistical results show that the average precisions (AP) of pedestrians and vehicles are increased by 4.5% and 2.6% respectively, compared with the benchmark of the YOLOv3 model trained on only the thermal images. The proposed model provides reliable technical support for the application of thermal images with UAV platforms.
Highlights
Image-based object detection techniques have been widely used in several applications, such as environmental monitoring, emergency management, and traffic survey
To address the challenge in unmanned aerial vehicles (UAV)-borne thermal images, we propose the use of boundary-aware saliency maps to enhance the data
The main contributions of this paper are as follows: (1) This paper presents a method of fusing the thermal images with their corresponding saliency maps
Summary
Image-based object detection techniques have been widely used in several applications, such as environmental monitoring, emergency management, and traffic survey. Most of the previous works focus on visible images, which might be influenced by illumination. Thermal images have the capability of observing objects at night or in bad lighting conditions. More researchers are exploring the potential of using thermal images to build intelligent systems (Portmann et al, 2014; Wang, Bai, 2019; Li et al, 2019; Sun et al, 2019). Thermal images have some defects, such as low contrast, edge blur, and strong noise, which make them less distinguishable. To address the challenge in UAV-borne thermal images, we propose the use of boundary-aware saliency maps to enhance the data
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