Title: Deep learning-based wall crack detection

Authors: Zujia Zheng; Kui Yang

Addresses: Office of Infrastructure, Wuhan University of Science and Technology, Wuhan, Hubei, 430081, China ' Office of Infrastructure, Wuhan University of Science and Technology, Wuhan, Hubei, 430081, China

Abstract: This study addresses the issues of high weight and complexity in the YOLOv4 network and presents an improved wall crack detection method based on it. The approach involves replacing YOLOv4's backbone feature extraction network with MobileNetV2 and employing deep separable convolution to reduce model complexity. Additionally, the SENet attention mechanism is integrated to counteract accuracy loss due to lightweighting. The study also includes data set construction and annotation. Experimental results demonstrate that this method significantly reduces network weight, parameters and computational requirements while maintaining high detection accuracy, making it suitable for various wall crack detection tasks.

Keywords: YOLOv4; wall crack detection; target detection.

DOI: 10.1504/IJWMC.2024.140271

International Journal of Wireless and Mobile Computing, 2024 Vol.27 No.2, pp.118 - 124

Received: 16 Sep 2023
Accepted: 07 Dec 2023
Published online: 01 Aug 2024 *

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