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http://hdl.handle.net/10397/90916
Title: | Concrete crack detection based on well-known feature extractor model and the YOLO_v2 network | Authors: | Teng, S Liu, Z Chen, G Cheng, L |
Issue Date: | Jan-2021 | Source: | Applied sciences, Jan. 2021, v. 11, no. 2, 813, p. 1-13 | Abstract: | This paper compares the crack detection performance (in terms of precision and computational cost) of the YOLO_v2 using 11 feature extractors, which provides a base for realizing fast and accurate crack detection on concrete structures. Cracks on concrete structures are an important indicator for assessing their durability and safety, and real-time crack detection is an essential task in structural maintenance. The object detection algorithm, especially the YOLO series network, has significant potential in crack detection, while the feature extractor is the most important component of the YOLO_v2. Hence, this paper employs 11 well-known CNN models as the feature extractor of the YOLO_v2 for crack detection. The results confirm that a different feature extractor model of the YOLO_v2 network leads to a different detection result, among which the AP value is 0.89, 0, and 0 for ‘resnet18', ‘alexnet’, and ‘vgg16', respectively meanwhile, the ‘googlenet’ (AP = 0.84) and ‘mobilenetv2' (AP = 0.87) also demonstrate comparable AP values. In terms of computing speed, the ‘alexnet’ takes the least computational time, the ‘squeezenet’ and ‘resnet18' are ranked second and third respectively; therefore, the ‘resnet18' is the best feature extractor model in terms of precision and computational cost. Additionally, through the parametric study (influence on detection results of the training epoch, feature extraction layer, and testing image size), the associated parameters indeed have an impact on the detection results. It is demonstrated that: excellent crack detection results can be achieved by the YOLO_v2 detector, in which an appropriate feature extractor model, training epoch, feature extraction layer, and testing image size play an important role. | Keywords: | Computational cost Crack detection Detection precision Feature extraction layer Feature extractor YOLO network |
Publisher: | Molecular Diversity Preservation International (MDPI) | Journal: | Applied sciences | ISSN: | 2076-3417 | DOI: | 10.3390/app11020813 | Rights: | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The following publication Teng, S.; Liu, Z.; Chen, G.; Cheng, L. Concrete Crack Detection Based onWell-Known Feature Extractor Model and the YOLO_v2 Network. Appl. Sci. 2021, 11, 813 is available at https://doi.org/10.3390/app11020813 |
Appears in Collections: | Journal/Magazine Article |
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