VNU-UET Repository

Hierarchical Convolutional Neural Network with Feature Preservation and Autotuned Thresholding for Crack Detection

Zhu, Qiuchen and Dinh, Tran Hiep and Phung, Manh Duong and Quang, Ha (2021) Hierarchical Convolutional Neural Network with Feature Preservation and Autotuned Thresholding for Crack Detection. IEEE Access . ISSN 2169-3536

Download (9MB) | Preview


Drone imagery is increasingly used in automated inspection for infrastructure surface defects, especially in hazardous or unreachable environments. In machine vision, the key to crack detection rests with robust and accurate algorithms for image processing. To this end, this paper proposes a deep learning approach using hierarchical convolutional neural networks with feature preservation (HCNNFP) and an intercontrast iterative thresholding algorithm for image binarization. First, a set of branch networks is proposed, wherein the output of previous convolutional blocks is half-sizedly concatenated to the current ones to reduce the obscuration in the down-sampling stage taking into account the overall information loss. Next, to extract the feature map generated from the enhanced HCNN, a binary contrast-based autotuned thresholding (CBAT) approach is developed at the post-processing step, where patterns of interest are clustered within the probability map of the identified features. The proposed technique is then applied to identify surface cracks on the surface of roads, bridges or pavements. An extensive comparison with existing techniques is conducted on various datasets and subject to a number of evaluation criteria including the average F-measure (AFβ) introduced here for dynamic quantification of the performance. Experiments on crack images, including those captured by unmanned aerial vehicles inspecting a monorail bridge. The proposed technique outperforms the existing methods on various tested datasets especially for GAPs dataset with an increase of about 1.4% in terms of AFβ while the mean percentage error drops by 2.2%. Such performance demonstrates the merits of the proposed HCNNFP architecture for surface defect inspection.

Item Type: Article
Subjects: Electronics and Communications
Electronics and Communications > Electronics and Computer Engineering
Information Technology (IT)
Civil Engineering
ISI-indexed journals
Divisions: Advanced Insitute of Engineering and Technology (AVITECH)
Faculty of Electronics and Telecommunications (FET)
Depositing User: Dr Manh Duong Phung
Date Deposited: 31 May 2021 10:58
Last Modified: 31 May 2021 10:58

Actions (login required)

View Item View Item