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A platform for research: civil engineering, architecture and urbanism
Deep Active Learning for Civil Infrastructure Defect Detection and Classification
Automatic detection and classification of defects in infrastructure surface images can largely boost its maintenance efficiency. Given enough labeled images, various supervised learning methods have been investigated for this task, including decision trees and support vector machines in previous studies, and deep neural networks more recently. However, in real world applications, labels are harder to obtain than images, due to the limited labeling resources (i.e., experts). Thus we propose a deep active learning system to maximize the performance. A deep residual network is firstly designed for defect detection and classification in an image. Following our active learning strategy, this network is trained as soon as an initial batch of labeled images becomes available. It is then used to select a most informative subset of new images and query labels from experts to retrain the network. Experiments demonstrate more efficient performance improvements of our method than baselines, achieving 87.5% detection accuracy.
Deep Active Learning for Civil Infrastructure Defect Detection and Classification
Automatic detection and classification of defects in infrastructure surface images can largely boost its maintenance efficiency. Given enough labeled images, various supervised learning methods have been investigated for this task, including decision trees and support vector machines in previous studies, and deep neural networks more recently. However, in real world applications, labels are harder to obtain than images, due to the limited labeling resources (i.e., experts). Thus we propose a deep active learning system to maximize the performance. A deep residual network is firstly designed for defect detection and classification in an image. Following our active learning strategy, this network is trained as soon as an initial batch of labeled images becomes available. It is then used to select a most informative subset of new images and query labels from experts to retrain the network. Experiments demonstrate more efficient performance improvements of our method than baselines, achieving 87.5% detection accuracy.
Deep Active Learning for Civil Infrastructure Defect Detection and Classification
Feng, Chen (author) / Liu, Ming-Yu (author) / Kao, Chieh-Chi (author) / Lee, Teng-Yok (author)
ASCE International Workshop on Computing in Civil Engineering 2017 ; 2017 ; Seattle, Washington
Computing in Civil Engineering 2017 ; 298-306
2017-06-22
Conference paper
Electronic Resource
English
Deep Active Learning for Civil Infrastructure Defect Detection and Classification
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