Efficient crack segmentation with multi-decoder networks and enhanced feature fusion

Identificador:  imarina:9452174

Handle: https://hdl.handle.net/20.500.11797/imarina9452174

Autores:  Okran, Ammar M; Rashwan, Hatem A; Saleh, Adel; Puig, Domenec

Resumen:
In infrastructure management, intelligent crack detection is vital, particularly for maintaining crucial elements such as road networks in urban areas. Detecting pavement defects promptly and accurately is essential for timely repairs and hazard prevention. However, this task is challenging due to factors, such as complex backgrounds, micro defects, diverse defect shapes and sizes, and class imbalance issues. Innovative approaches and advanced technologies are needed to address these challenges and effectively manage infrastructure complexities. In this study, we propose a novel framework for crack segmentation, called CrackMaster. CrackMaster utilizes advanced neural network architectures, leveraging the next generation of convolutional networks (ConvNeXt) as an encoder and dual decoders customized for distinct tasks. The first decoder adopts a self-supervised learning paradigm to reconstruct images, thereby enhancing feature extraction capabilities. Meanwhile, the second decoder combines deep labelling network for semantic image segmentation (Deeplabv3+) with a light deep neural network (LinkNet) to facilitate precise segmentation. Notably, we introduce an Enhanced Feature Fusion (EFF) block to improve features quality, enhancing information flow and context preservation, thus boosting segmentation performance. Experimental results conducted on three diverse datasets, including our in-house Road Crack Dataset (RCD), DeepCrack537, and Yang Crack Dataset (YCD) datasets, demonstrate the effectiveness of our framework achieving outstanding Intersection over Union scores (IoU) of 86.0%, 87.8%, and 76.9%, respectively, showing superior accuracy and robustness in crack segmentation tasks. These findings underscore the potential applicability of our framework in real-world infrastructure management scenarios. The code is publicly available at: https://github.com/AmmarOkran/ CrackMaster.