Low Contrast Infrared Target Detection Method Based on Residual Thermal Backbone Network and Weighting Loss Function

Abstract

Infrared (IR) target detection is an important technology in the field of remote sensing image application. The methods for IR image target detection are affected by many characteristics, such as poor texture information and low contrast. These characteristics bring great challenges to infrared target detection. To address the above problem, we propose a novel target detection method for IR images target detection in this paper. Our method is improved from two aspects: Firstly, we propose a novel residual thermal infrared network (ResTNet) as the backbone in our method, which is designed to improve the feature extraction ability for low contrast targets by Transformer structure. Secondly, we propose a contrast enhancement loss function (CTEL) that optimizes the weights about the loss value of the low contrast targets’ prediction results to improve the effect of learning low contrast targets and compensate for the gradient of the low-contrast targets in training back propagation. Experiments on FLIR-ADAS dataset and our remote sensing dataset show that our method is far superior to the state-of-the-art ones in detecting low-contrast targets of IR images. The mAP of the proposed method reaches 84% on the FLIR public dataset. This is the best precision in published papers. Compared with the baseline, the performance on low-contrast targets is improved by about 20%. In addition, the proposed method is state-of-the-art on the FLIR dataset and our dataset. The comparative experiments demonstrate that our method has strong robustness and competitiveness.