An Infrared Small Target Detection Method Using Segmentation Based Region Proposal and CNN

Previous infrared small target detection approaches mainly solve the problem of detecting small target in sky background with strong cloud occlusion. However, these methods hardly exclude the negative objects other than cloud that cause false alarms. To address this problem, we propose an infrared small target detection framework using segmentation based region proposal and Convolution Neural Network (SCNN). In specific, an improved segmentation algorithm is used to obtain the salient regions from the background as the proposals. To reduce the high false alarms from proposals, a lightweight CNN is used to classify these regions and make final predictions. Owning to the lack of current public infrared small target datasets, a new infrared dataset is proposed in this paper. The experimental results demonstrate that the proposed method has a good performance in detection rate and false alarm rate.