Year: 2024
Type: article
Abstract: With the progress of social life, the aging of building facilities has become an inevitable phenomenon. The efficiency of manual crack detection is limited, so it is necessary to explore intelligent detection technology. This article proposes a novel crack detection method TF-MobileNet. We took into account the effect of lightweight and crack feature extraction, so we developed a novel crack feature extraction backbone network, which combined Transformer and MobileNetV3. Then we improved the feature fusion network by using the multi-headed attention mechanism of the Bottleneck Transformer, which enables the feature fusion effect to be improved. Then, we integrated SENet and SimAM attention mechanisms into the networks used for feature extraction and feature fusion, thereby further improving the crack detection performance. Finally, we deployed our model in edge devices (NVIDIA Jeston Nano). The findings indicate that our proposed model has achieved 90.8% mAP on the dataset and worked well on the edge device side, which meet the requirements of automatic crack detection. Our model enables real-time monitoring of pavement using edge devices. This approach allows for timely maintenance and repair of the pavement. In the future, we can train the model to recognize more pavement distress features, addressing road safety issues effectively.
Source: Applied Sciences
DOI: 10.3390/app14199004
Authors: Jiantao Yu, Songrong Qian, Cheng Chen
Institution: Guizhou University
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,728
Funder: Guizhou University
OpenAlex ID: W4403188953