Effective helmet detection is critical for construction safety but challenging to deploy in resource-limited environments. This paper proposes an enhanced object detector based on YOLO11, integrating Deformable Weighted Residual (DWR) modules and a BIdirectional Feature Pyramid Network (BIFPN) architecture. This integration significantly reduces computational demands while improving accuracy over YOLO11. Comprehensive experiments confirm that our proposed model outperforms other leading detection methods, providing a highly accurate, efficient solution for real-time helmet detection in resource-constrained environments.