Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Buildings

DOI: 10.3390/buildings15162850

Authors: Fei Yu, Liangyu Yuan, Qiang Jin, Di Hu

Institution: Xinjiang Agricultural University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137