An infrared small target detection model via Gather-Excite attention and normalized Wasserstein distance

Abstract

Infrared small target detection (ISTD) is the main research content for defense confrontation, long-range precision strikes and battlefield intelligence reconnaissance. Targets from the aerial view have the characteristics of small size and dim signal. These characteristics affect the performance of traditional detection models. At present, the target detection model based on deep learning has made huge advances. The You Only Look Once (YOLO) series is a classic branch. In this paper, a model with better adaptation capabilities, namely ISTD-YOLOv7, is proposed for infrared small target detection. First, the anchors of YOLOv7 are updated to provide prior. Second, Gather-Excite (GE) attention is embedded in YOLOv7 to exploit feature context and spatial location information. Finally, Normalized Wasserstein Distance (NWD) replaces IoU in the loss function to alleviate the sensitivity of YOLOv7 for location deviations of small targets. Experiments on a standard dataset show that the proposed model has stronger detection performance than YOLOv3, YOLOv5s, SSD, CenterNet, FCOS, YOLOXs, DETR and the baseline model, with a mean Average Precision (mAP) of 98.43%. Moreover, ablation studies indicate the effectiveness of the improved components.

Keywords

• infrared small target detection
• yolov7
• anchor update
• gather-excite attention
• normalized wasserstein distance