Context-CrackNet: A Context-Aware Framework for Precise Segmentation of Tiny Cracks in Pavement images

TL;DR

Context-CrackNet is a new deep learning framework that improves tiny crack segmentation in pavement images by capturing fine details and global context, outperforming existing models with high accuracy and efficiency.

Contribution

The paper introduces Context-CrackNet, a novel encoder-decoder architecture with RFEM and CAGM modules for enhanced crack segmentation performance.

Findings

Outperforms 9 state-of-the-art models in segmentation metrics

Achieves higher mIoU and Dice scores with efficient inference

Ablation studies confirm the effectiveness of RFEM and CAGM modules

Abstract

The accurate detection and segmentation of pavement distresses, particularly tiny and small cracks, are critical for early intervention and preventive maintenance in transportation infrastructure. Traditional manual inspection methods are labor-intensive and inconsistent, while existing deep learning models struggle with fine-grained segmentation and computational efficiency. To address these challenges, this study proposes Context-CrackNet, a novel encoder-decoder architecture featuring the Region-Focused Enhancement Module (RFEM) and Context-Aware Global Module (CAGM). These innovations enhance the model's ability to capture fine-grained local details and global contextual dependencies, respectively. Context-CrackNet was rigorously evaluated on ten publicly available crack segmentation datasets, covering diverse pavement distress scenarios. The model consistently outperformed 9 state-of-the-art segmentation frameworks, achieving superior performance metrics such as mIoU and Dice score, while maintaining competitive inference efficiency. Ablation studies confirmed the complementary roles of RFEM and CAGM, with notable improvements in mIoU and Dice score when both modules were integrated. Additionally, the model's balance of precision and computational efficiency highlights its potential for real-time deployment in large-scale pavement monitoring systems.