BOM-YOLOv12n: A Lightweight Detection Model for Protective Equipment in Smart Construction Sites

Abstract

In complex construction site environments, protective equipment detection suffers from large target scale variations, frequent occlusions, and strong background interference, making it difficult for existing lightweight models to balance detection accuracy with computational cost. To address these challenges, an enhanced lightweight detection model termed BOM-YOLOv12n is introduced. Based on YOLOv12n, a BN-calibrated re-parameterized convolution (BCRepConv) is first designed to reduce the feature distribution gap between training and inference and enhance small-target feature representation. A multi-dimensional attention module ODC2f is then introduced to strengthen local discriminative responses under occlusion. Finally, the MPDIoU loss based on minimum point distance is adopted to improve bounding box regression accuracy. Experimental results on the HardHat-Vest dataset show that BOM-YOLOv12n achieves an mAP0.5 of 87.3% and an mAP0.5:0.95 of 58.5%, with only 2.3 M parameters and 6.2 GFLOPs, achieving a favorable balance between detection accuracy and computational efficiency compared to the baseline and other mainstream lightweight detectors. The experimental results indicate that BOM-YOLOv12n can serve as a competitive and efficient solution for real-time protective equipment detection in smart construction sites.