Abstract
To enhance the precision and reliability of road crack detection, this study introduces an innovative neural network architecture. Strategies were implemented to effectively address the issue of overfitting resulting from the intricacy of the proposed SegCrackNet. Dropout layers, multi-level output fusion, and T-bridge block structures are employed in the network. This optimization allows for a more comprehensive exploitation of contextual information, demonstrating its instrumental role in the efficient detection of subtle variations. Experimental findings clearly demonstrate substantial improvements when compared to other network models. On the Crack500, Crack200, and pavement images datasets, remarkable enhancements in the average Intersection over Union (IoU) scores were observed, with increases of 4.3%, 9.4%, and 3.7%, respectively.
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