Identification of Crack in Concrete Structures Based on MobileNetV2 of Lightweight Convolutional Network

FAN Lijun

doi:10.13204/j.gyjzG23021618

Volume 53 Issue 7

Jul. 2023

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Article Navigation > INDUSTRIAL CONSTRUCTION > 2023 > 53(7): 231-236

FAN Lijun. Identification of Crack in Concrete Structures Based on MobileNetV2 of Lightweight Convolutional Network[J]. INDUSTRIAL CONSTRUCTION, 2023, 53(7): 231-236. doi: 10.13204/j.gyjzG23021618

Citation:

FAN Lijun. Identification of Crack in Concrete Structures Based on MobileNetV2 of Lightweight Convolutional Network[J]. INDUSTRIAL CONSTRUCTION, 2023, 53(7): 231-236. doi: 10.13204/j.gyjzG23021618

FAN Lijun. Identification of Crack in Concrete Structures Based on MobileNetV2 of Lightweight Convolutional Network[J]. INDUSTRIAL CONSTRUCTION, 2023, 53(7): 231-236. doi: 10.13204/j.gyjzG23021618

Citation:

FAN Lijun. Identification of Crack in Concrete Structures Based on MobileNetV2 of Lightweight Convolutional Network[J]. INDUSTRIAL CONSTRUCTION, 2023, 53(7): 231-236. doi: 10.13204/j.gyjzG23021618

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Identification of Crack in Concrete Structures Based on MobileNetV2 of Lightweight Convolutional Network

doi: 10.13204/j.gyjzG23021618

FAN Lijun^1,2

1. China Railway Construction Group Co., Ltd., Beijing 100043, China;
2. Engineering Research Institute, China Railway Construction Group Co., Ltd., Beijing 100043, China

Received Date: 2023-02-16

Abstract

Abstract

With the increase of service time of concrete structures, the generated cracks will continue to expand and may cause damage to the structure. Therefore, crack detection is of great significance for the health monitoring of concrete structures, but traditional ultrasonic and artificial vision-based detection methods can not quickly classify cracks. Based on MobileNetV2 lightweight convolutional network and TensorFlow deep learning framework, a prediction model for rapid identification and classification of concrete structure crack was established. Firstly, the data set was extracted and partitioned based on the pathlib method; secondly, data enhancement based on transfer learning expanded the data set; thirdly, based on the TensorFlow framework, Keras was used to build the pooling layer of the convolutional network; finally, a complete convolutional network architecture and obtained the results were obtained. The test results showed that the model converged, the prediction accuracy reached 0.997 5, and the training time was only 710 s. It could provide help for mobile equipment detection in the project site.
- deep learning,
- convolutional network,
- concrete structures,
- MobileNetV2,
- crack

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References(18)

References

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