Network for robust and high-accuracy pavement crack segmentation

Zhang, Yingchao; Liu, Cheng

doi:10.1016/j.autcon.2024.105375

Cited by 7 publications

(1 citation statement)

References 40 publications

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“…Bai et al [26] designed a dual-path crack extraction network, enhancing the ability to describe complex crack features. Zhang et al [27] proposed a network based on deformable convolution to adapt to the morphology of cracks. However, the following issues are still common:…”

Section: Introductionmentioning

confidence: 99%

GGMNet: Pavement-Crack Detection Based on Global Context Awareness and Multi-Scale Fusion

Wang,

He,

Zeng

et al. 2024

Remote Sensing

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Accurate and comprehensive detection of pavement cracks is important for maintaining road quality and ensuring traffic safety. However, the complexity of road surfaces and the diversity of cracks make it difficult for existing methods to accomplish this challenging task. This paper proposes a novel network named the global graph multiscale network (GGMNet) for automated pixel-level detection of pavement cracks. The GGMNet network has several innovations compared with the mainstream road crack detection network: (1) a global contextual Res-block (GC-Resblock) is proposed to guide the network to emphasize the identities of cracks while suppressing background noises; (2) a graph pyramid pooling module (GPPM) is designed to aggregate the multi-scale features and capture the long-range dependencies of cracks; (3) a multi-scale features fusion module (MFF) is established to efficiently represent and deeply fuse multi-scale features. We carried out extensive experiments on three pavement crack datasets. These were DeepCrack dataset, with complex background noises; the CrackTree260 dataset, with various crack structures; and the Aerial Track Detection dataset, with a drone’s perspective. The experimental results demonstrate that GGMNet has excellent performance, high accuracy, and strong robustness. In conclusion, this paper provides support for accurate and timely road maintenance and has important reference values and enlightening implications for further linear feature extraction research.

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Section: Introductionmentioning

confidence: 99%