MBAV: A Positional Encoding-Based Lightweight Network for Detecting Embedded Parts in Prefabricated Composite Slabs

• Published in: Buildings (Basel) Vol. 15; no. 16; p. 2850
• Main Authors: Yu, Fei, Yuan, Liangyu, Jin, Qiang, Hu, Di
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2025
• Subjects: Ablation; Accuracy; Algorithms; Artificial intelligence; Coding; Composite materials; Concrete; Concrete slabs; Datasets; deep learning; Factories; Inspection; Inspections; object detection; positional encoding; prefabricated composite slabs; Prefabrication; Quality control equipment; Real time; Slab casting; Structural engineering; Structural safety
• ISSN: 2075-5309; 2075-5309
• DOI: 10.3390/buildings15162850

The accurate detection of embedded parts and truss rebars in prefabricated concrete composite slabs before casting is essential in ensuring structural safety and reliability. However, traditional inspection methods are time-consuming and lack real-time monitoring capabilities, limiting their suitability for modern prefabrication workflows. To address these challenges, this study proposes MBAV, a lightweight object detection model for the quality inspection of prefabricated concrete composite slabs. A dedicated dataset was built to compensate for the absence of public data and to provide sufficient training samples. The proposed model integrates positional encoding into a lightweight architecture to enhance its ability to capture multiscale features in complex environments. Ablation and comparative experiments on the self-constructed dataset show that MBAV achieves an mAP50 of 91% with a model size of only 5.7 MB—8% smaller than comparable models. These results demonstrate that MBAV is accurate and efficient, with its lightweight design showing strong potential for real-time quality inspection in prefabricated concrete production.