Lightweight Multi-Object Detection for Construction Sites Based on YOLO-World

Abstract

Addressing the current issues in construction site detection algorithms—such as missed detections, false positives, and high model complexity—caused by occlusions and scale variations in dense environments. This paper proposes a lightweight multi-object detection model for construction sites based on YOLO-World, named the LCS-YOLO model, to achieve a balance between detection efficiency and accuracy. We propose the RGNet (Re-parameterization GhostNet) module, which integrates re-parameterized convolutions and a multi-branch architecture. This approach addresses the issue of information redundancy in intermediate feature maps while enhancing feature extraction and gradient flow capabilities. Combined with the adaptive downsampling module ADown (Adaptive Downsampling), it better captures image features and achieves spatial compression, reducing model complexity while enhancing interaction between images and text. Experiments demonstrate that the LCS-YOLO model outperforms other comparison models in overall performance, achieving a balance between accuracy and efficiency.