Infrared Small Target Detection Based on DETR Architecture and Super-Resolution Technique

Infrared small target detection (ISTD) holds significant importance in domains such as maritime search and rescue, and autonomous driving. To enhance the detection capabilities of infrared small targets against complex backgrounds, a novel detection algorithm based on an improved Detection Transformer (DTER) is proposed. This algorithm leverages the DTER detection framework and the EDSR network, utilizing super-resolution reconstructed images as inputs. It incorporates the Enhanced Multi-Scale Attention (EMA) module and an improved backbone structure. Moreover, it employs a micro-target detection encoder head with a new feature layer S2 to elevate the quality of minute feature extraction. The proposed method achieved a mAP@50 of 96% and mAP@(50:95) of 54.6% on a public dataset. Compared to current state-of-the-art methods for infrared small target detection, it demonstrates superior capabilities in reducing false positives and misses while maintaining commendable real-time performance.