CA-U2-Net: Contour Detection and Attention in U2-Net for Infrared Dim and Small Target Detection

With the development of infrared technology, infrared dim and small target detection plays an essential role in precision guidance and early warning systems. Due to the low contrast and signal-to-noise ratio that characterizes infrared dim and small target in images, the dim and small target can easily be drowned out by noise and background. A new infrared dim and small target detection network (CA-U2-Net) is proposed to address the challenge of infrared weak target detection and shape retention in complex backgrounds. Specifically, firstly, the U2-Net network structure has been improved to prevent the loss of shallow information due to increased network depth and to make it more suitable for detecting the dim and small target. Then, the upper and lower attention module was designed on the network to make the model more focused on dim and small target features while suppressing irrelevant information, further improving the detection rate. Finally, a contour detection branch was added to the top of the model to fuse the contour detection map with the feature map to get a better target shape. After experimental evaluation, the method achieved a detection rate of 97.17% and retained a more accurate infrared dim and small target shape. Compared to other advanced methods, our method performs better in detection rate, false detection rate and shape retention. In addition, a new infrared dim and small target dataset consisting of 10,000 images was constructed.