Recognition method of bridge apparent defects based on image processing and improved convolutional neural networks
Sheng Li, Zhousheng Chang, Xiaodan Zhou
TL;DR
This paper introduces a new model for detecting bridge defects using image processing and improved neural networks, achieving high accuracy and efficiency.
Contribution
The novel contribution is an improved CNN-based model for bridge defect detection with enhanced localization and accuracy.
Findings
The model achieved 98.2% detection accuracy and 97.8% size calculation accuracy.
Positioning accuracy improved by 5.46% compared to previous methods.
The model's response time was 1.9 seconds with low false detection rate.
Abstract
As an important transportation hub, the detection of appearance defects in bridges has been characterized by low accuracy and low efficiency. To address this problem, the study proposes a bridge appearance defect recognition model based on image processing and improved convolutional neural network. The model is divided into three different modules. The first module is the bridge appearance defect classification and recognition module based on transfer learning and convolutional network. The second module further localizes the region of defective cracks based on the classification and recognition results. This module uses improved fast region convolutional neural network for region segmentation to further determine the location of cracks in the image. Finally, operations such as corrosion and expansion are performed on the cracks through morphological theory to further extract the crack…
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Taxonomy
TopicsInfrastructure Maintenance and Monitoring · Advanced Neural Network Applications · Concrete Corrosion and Durability