Adaptive Frame Sampling and Feature Alignment for Multi-Frame Infrared Small Target Detection

Abstract

In recent years, infrared images have attracted widespread attention, due to their extensive application in low-visibility search and rescue, forest fire monitoring, ground target monitoring, and other fields. Infrared small target detection technology plays a vital role in these applications. Although there has been significant research over the years, accurately detecting infrared small targets in complex backgrounds remains a significant challenge. Multi-frame detection methods can significantly improve detection performance in these cases. However, current multi-frame methods face difficulties in balancing the number of input frames and detection speed, and cannot effectively handle the background motion caused by movement of the infrared camera. To address these issues, we propose an adaptive frame sampling method and a detection network aligned at the feature level. Our adaptive frame sampling method uses mutual information to measure motion changes between adjacent frames, construct a motion distribution, and sample frames with uniform motion based on the averaged motion distribution. Our detection network handles background motion by predicting a homography flow matrix that aligns features at the feature level. Extensive evaluation of all components showed that the proposed method can more effectively perform multi-frame infrared small target detection.