Weak feature crack detection in high-resolution concrete dam surface image with LarkMNet

Abstract

Weak feature crack detection is crucial for dam structural health. Existing methods for detecting weak feature cracks in high-resolution images continue to pose challenges. This paper proposes a Perception Large Kernel-based Multi-level Gather-and-Distribute Network (LarkMNet) for weak-feature crack detection in high-resolution images. The construction of a Perception Large-Kernel ConvNet (LarkNet) enables feature extraction and expansion of the receptive field in high-resolution images. The design of the Multi-level Gather-and-Distribute Network (MGDNet) aims to enhance the critical characteristics of weak cracks. Introducing a Shared Detection Head and an Inner-SCYLLA-Intersection over Union further enhances the model’s detection accuracy. Experimental results demonstrate that LarkMNet’s detection accuracy in high-resolution image datasets surpasses that of mainstream detection models, achieving an F1 score and mAP of 89.4% and 93.3%, respectively. Furthermore, the method’s validity has been confirmed through high-resolution image testing and UAV on-site inspections, efficiently and accurately detecting weak feature cracks in high-resolution images.