CN-UNet: ConvNeXt UNet With Slicing-Aided Hyper Segmentation for Infrared Small Target Detection

Infrared small target detection (IRSTD) has been a long challenging task in infrared detection technology due to the inherently low contrast and absence of discernible texture in such targets. To address this issue, a new U-shaped segmentation network (CN-UNet) is proposed in this article. CN-UNet first employs the ConvNeXt module as the encoder, which implements a self-attention mechanism through pure convolutional operations, mirroring the Swin Transformer’s functionality while expanding the receptive field interactions of target features. This design not only optimizes computational efficiency but also enables multiscale feature identification across diverse dimensional contexts. Besides, a multiscale local contrast learning module (MSLCLM) is integrated into the encoder-decoder cross layer connections to selectively filter features aligned with human visual priors, thereby directing the network’s attention toward precise target localization. Finally, during the model inference, a slicing-aided hypersegmentation module is introduced to dynamically rescale output images through adaptive slicing operations, which could furtherly improve infrared target detection and segmentation performance. Comprehensive evaluations across three typical benchmarks demonstrate that the proposed CN-UNet is superior to other state-of-the-art IRSTD methods, with better visual quality and index evaluation metrics.