An ultra-lightweight network combining Mamba and frequency-domain feature extraction for pavement tiny-crack segmentation

Abstract

Pixel-level detection and segmentation of pavement cracks is a crucial but challenging task. Current deep learning methods mainly target larger cracks, often overlooking tiny cracks. Additionally, improvements in these methods typically involve more complex network structures, leading to reduced efficiency and higher computational demands. To tackle these challenges, we propose an Ultra-lightweight Network (ULNet) designed for tiny crack segmentation. ULNet boasts a low parameter count and minimal FLOPs, comprising three main components: the Mamba feature extraction branch, the frequency domain feature extraction branch, and the feature decoder. The Mamba feature extraction branch utilizes the proposed cross-visual Mamba feature extraction module, which enhances the accuracy of tiny crack detection by extracting global features through layer-by-layer processing and cross-fusion of feature information, all without relying on an attention mechanism. The frequency domain feature extraction branch transforms input features into the frequency domain and applies a Gaussian high-pass filter to smooth the image and extract high-frequency crack details while suppressing noise. Finally, the feature decoder incorporates a multi-scale feature selection and fusion module that integrates both channel and spatial attention, enabling effective extraction of tiny crack features from adjacent scales. Inspired by the mechanisms of biological visual information processing, we integrate the high-frequency features from the frequency domain feature extraction branch with the features from the Mamba feature extraction branch. This integration follows the interactive patterns of biological vision, allowing for layer-wise fusion and interaction of features, thereby supplementing the information from the Mamba branch and reducing information loss. We tested and evaluated ULNet on several public datasets (BJN260, Rain365, Sun520, and DeepCrack). Experimental results indicate that our method achieves state-of-the-art performance among all lightweight approaches.