Defect Detection of Prefabricated Building Components With Integrated YOLOv5s-GhostNet

The defect detection of prefabricated building components during production and use is the key to ensuring building quality. Traditional manual methods are inefficient and prone to missed detections, while existing deep learning models face challenges such as insufficient detection accuracy, high computational resource consumption, and poor real-time performance in complex backgrounds and small target scenes. To address this issue, this study uses YOLOv5 as the basic object detection algorithm, introduces an attention mechanism, replaces the loss function, and improves the Adam optimizer to obtain YOLOv5s. To reduce the size of the YOLOv5s, GhostNet is introduced as a feature extraction network to construct the YOLOv5s-GhostNet detection model for defect detection of prefabricated building components. In actual defect detection of building components, the YOLOv5s-GhostNet researched and designed only had 12 defect classification errors, and the classification accuracy was improved to 99.6%. The proposed model had an AUC area of 0.90 for defect detection in building components and a detection speed of 42FPS. In visual analysis, the confidence coefficient of the proposed model for detecting “crack” defects was 99%. YOLOv5s-GhostNet has extremely high accuracy and efficiency in detecting defects in prefabricated building components and has broad application prospects. In order to verify the performance of the YOLOv5s GhostNet model proposed in this article, traditional YOLOv5, VGG-16, ResNet, and HRNet were selected as benchmark models for comparison. The experimental results show that the proposed model outperforms these benchmark models in terms of detection accuracy, detection speed, and model complexity, further verifying its effectiveness and superiority in practical applications. The application of this model not only promotes the further development of deep learning technology in architecture but also provides a reference for defect detection in other fields.