MDAFNet: Multiscale Differential Edge and Adaptive Frequency Guided Network for Infrared Small Target Detection

Infrared small target detection (IRSTD) plays a crucial role in numerous military and civilian applications. However, existing methods often face the gradual degradation of target edge pixels as the number of network layers increases, and traditional convolution struggles to differentiate between frequency components during feature extraction, leading to low-frequency backgrounds interfering with high-frequency targets and high-frequency noise triggering false detections. To address these limitations, we propose MDAFNet (Multi-scale Differential Edge and Adaptive Frequency Guided Network for Infrared Small Target Detection), which integrates the Multi-Scale Differential Edge (MSDE) module and Dual-Domain Adaptive Feature Enhancement (DAFE) module. The MSDE module, through a multi-scale edge extraction and enhancement mechanism, effectively compensates for the cumulative loss of target edge information during downsampling. The DAFE module combines frequency domain processing mechanisms with simulated frequency decomposition and fusion mechanisms in the spatial domain to effectively improve the network's capability to adaptively enhance high-frequency targets and selectively suppress high-frequency noise. Experimental results on multiple datasets demonstrate the superior detection performance of MDAFNet.

Multi-Level Feature Aggregation and Recursive Alignment Network for Real-Time Semantic Segmentation

Real-time semantic segmentation is a crucial research for real-world applications. However, many methods lay particular emphasis on reducing the computational complexity and model size, while largely sacrificing the accuracy. In some scenarios, such as autonomous navigation and driver assistance system, accuracy and speed are equally important. To tackle this problem, we propose a novel Multi-level Feature Aggregation and Recursive Alignment Network (MFARANet), aiming to achieve high segmentation accuracy at real-time inference speed. We employ ResNet-18 as the backbone to ensure efficiency, and propose three core components to compensate for the reduced model capacity due to the shallow backbone. Specifically, we first design Multi-level Feature Aggregation Module (MFAM) to aggregate the hierarchical features in the encoder to each scale to benefit subsequent spatial alignment and multi-scale inference. Then, we build Recursive Alignment Module (RAM) by combining the flow-based alignment module with recursive upsampling architecture for accurate and efficient spatial alignment between multi-scale score maps. Finally, the Adaptive Scores Fusion Module (ASFM) is proposed to adaptively fuse multi-scale scores so that the final prediction can favor objects of multiple scales. Comprehensive experiments on three benchmark datasets including Cityscapes, CamVid and PASCAL-Context show the effectiveness and efficiency of our method. In particular, we achieve a better balance between speed and accuracy than state-of-the-art real-time methods on Cityscapes and CamVid datasets. Code is available at: https://github.com/Yanhua-Zhang/MFARANet.