Context-CrackNet: A Context-Aware Framework for Precise Segmentation of Tiny Cracks in Pavement images
This addresses the need for early detection of pavement distresses for transportation infrastructure maintenance, though it appears incremental as it builds on existing encoder-decoder architectures with new modules.
The study tackled the problem of accurately segmenting tiny cracks in pavement images by proposing Context-CrackNet, which outperformed 9 state-of-the-art models on ten datasets with superior mIoU and Dice scores while maintaining competitive inference efficiency.
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.