Real-Time Ground-Level Building Damage Detection Based on Lightweight and Accurate YOLOv5 Using Terrestrial Images
نویسندگان
چکیده
Real-time building damage detection effectively improves the timeliness of post-earthquake assessments. In recent years, terrestrial images from smartphones or cameras have become a rich source disaster information that may be useful in assessing at lower cost. this study, we present an efficient method based on combination with improved YOLOv5. We compiled Ground-level Detection Building Damage Assessment (GDBDA) dataset consisting annotations types, including debris, collapse, spalling, and cracks. A lightweight accurate YOLOv5 (LA-YOLOv5) model was used to optimize efficiency accuracy. particular, Ghost bottleneck added backbone neck modules model, aim reduce size. Convolutional Block Attention Module (CBAM) module enhance recognition effect. addition, regarding scale difference damage, Bi-Directional Feature Pyramid Network (Bi-FPN) for multi-scale feature fusion aggregate features different types. Moreover, depthwise separable convolution (DSCONV) further compress parameters. Based our GDBDA dataset, proposed not only achieved accuracy above 90% targets, but also had smallest weight size fastest speed, which by about 64% 24%, respectively. The performed well datasets regions. overall results indicate realizes rapid detection, meets requirement embedding future.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2022
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs14122763