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- 저자 : Chuncheng Feng (검색결과 2 건)
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Structural Damage Detection using Deep Convolutional Neural Network and Transfer Learning
Chuncheng Feng,Hua Zhang,Shuang Wang,Yonglong Li,Haoran Wang,Fei Yan 대한토목학회 2019 KSCE JOURNAL OF CIVIL ENGINEERING Vol.23 No.10
During the long-term operation of hydro-junction infrastructure, water flow erosion causes concrete surfaces to crack, resulting in seepage, spalling, and rebar exposure. To ensure infrastructure safety, detecting such damage is critical. We propose a highly accurate damage detection method using a deep convolutional neural network with transfer learning. First, we collectedimages from hydro-junction infrastructure using a high-definition camera. Second, we preprocessed the images using an imageexpansion method. Finally, we modified the structure of Inception-v3 and trained the network using transfer learning to detectdamage. The experiments show that the accuracy of the proposed damage detection method is 96.8%, considerably higher thanthe accuracy of a support vector machine. The results demonstrate that our damage detection method achieves better damagedetection performance.
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Research on Crack Segmentation Method of Hydro-Junction Project Based on Target Detection Network
Jie Pang,Hua Zhang,Chuncheng Feng,Linjing Li 대한토목학회 2020 KSCE JOURNAL OF CIVIL ENGINEERING Vol.24 No.9
The defect detection is an important task for maintaining the hydro-junction project. A two-stage crack defect segmentation method based on target detection network is proposed to solve the problem of severe brightness imbalance and large noise in dam surface images. In the first stage, to improve the ability to locate crack areas, Inception Resnet V2 is used as feature extraction network to help Faster-RCNN extract more effective deep features, and the brightness, contrast of image is randomly adjusted before training. In the second segmentation stage, the crack areas are segmented at pixel-level using K-means. The experimental results on the self-made crack image dataset show that the location accuracy (AP) of the crack areas can be improved by 1.9%, reaching 96.8%, compared with other segmentation networks that do not locate crack areas, the intersection over union for segmentation of cracks (Iou) of the final segmentation results is at least 9.4% higher, reaching 52.7%. This method can provide effective technical support for inspection work of hydro-junction project.
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