Abstract：The DETR series networks based on the Transformer architecture keep pushing the boundaries of object detection accuracy and speed in computer vision. However, non-cooperative object detection applications based on infrared images face challenges because of environmental complexity and poor image quality. To solve this problem, a novel object detection algorithm with high detection accuracy was proposed in this study, utilizing the Deformable DETR as the baseline. Initially, an image enhancement module called CLAHE-GB was designed to enhance the image process on infrared images, and it was effectively integrated with Deformable DETR. Subsequently, the algorithm was pre-trained on a large-scale general dataset. Then, data augmentation and transfer learning methods were developed to retrain the parameters of the detection head network using a self-made dataset of small infrared images of aerial objects. Finally, a comprehensive result analysis was conducted. The results show that the proposed algorithm can successfully achieve promising image enhancement effects and detection accuracy on infrared image data.
张晓宇, 杜祥润, 张佳梁, 檀盼龙, 杨诗博. 基于Deformable DETR的红外图像目标检测方法研究[J]. 空天防御, 2024, 7(1): 16-23.
ZHANG Xiaoyu, DU Xiangrun, ZHANG Jialiang, TAN Panlong, YANG Shibo. Research on Infrared Image Object Detection Method Based on Deformable DETR. Air & Space Defense, 2024, 7(1): 16-23.
