Abstract
Infrared weak and small target detection technology has attracted much attention in recent years and is crucial in the application fields of early warning, monitoring, medical diagnostics, and anti-UAV detection.With the advancement of deep learning, CNN-based methods have achieved promising results in general-purpose target detection due to their powerful modeling capabilities; however, CNN-based methods cannot be directly applied to infrared small targets due to the disappearance of deep targets caused by multiple downsampling operations. Aiming at these problems, we proposed an improved dense nesting and attention infrared small target detection method based on U-Net called IDNA-UNet. A dense nested interaction module (DNIM) is designed as a feature extraction module to achieve level-by-level feature fusion and retain small targets' features and detailed positioning information. To integrate low-level features into deeper high-level features, we designed a bottom-up feature pyramid fusion module, which can further retain high-level semantic information and target detail information. In addition, a more suitable scale and position sensitive (SLS) loss is applied to each prediction scale to help the detector locate the target more accurately and distinguish different scales of the target. With our IDNA-UNet, the contextual information of small targets can be well incorporated and fully exploited by repetitive fusion and enhancement. Compared with existing methods, IDNA-UNet has achieved significant advantages in the intersection over union (IoU), detection probability (Pd), and false alarm rate (Fa) of infrared small target detection.