CBTi-YOLOv5: Improved YOLOv5 with CBAM, Transformer, and BiFPN for Real-Time Safety Helmet Detection - Dharmawan, Tio; Setiawan, Danang; Hidayat, Muhamad Arief; Widartha, Vandha Pradwiyasma (Universitas Airlangga, 2025)
Background: Some construction workers are often in a situation where injuries can occur from negligence in the use of safety helmets. To avoid this, supervision of the use of safety helmets should be conducted continuously during the work process through the application of computer vision technology. However, the complex background of the construction environment is a challenge to detecting small and densely packed safety helmets accurately. Objective: The construction environment is complex, and the wide workspace allows workers to be in an area far from supervision. The process makes it difficult for models to detect the use of safety helmets in complex, wide, and very high object density construction environments. Therefore, this study aims to overcome the problem by modifying YOLOv5s (You Only Look Once version 5) architecture. Methods: Real-time monitoring of the use of safety helmets could be performed using YOLOv5. This study proposed a modified YOLOv5s model called CBTi-YOLOv5s. The model incorporated Convolutional Block Attention Module (CBAM), Transformer, and Bi-directional Feature Pyramid Network (BiFPN) to improve feature extraction, multi-scale object representation, as well as detection accuracy, specifically on small and high-density objects in complex construction environments. Results: The results showed the modified YOLOv5s architecture had made an improvement of 3.7% in mean average precision (mAP) compared to the base YOLOv5s model. mAP of the base YOLOv5s model was 93.6%, while the modified CBTi-YOLOv5s model achieved 97.3%. The proposed modified YOLOv5s model also achieved an inference speed of 58 frames per second (FPS), and the base model achieved 104 FPS. Conclusion: CBTi-YOLOv5s improved the accuracy, mAP, and ability to detect objects of varying scales. However, this improvement had drawbacks, namely increased model size and decreased inferential speed due to increased model architectural complexity.. Keywords: Bi-FPN, CBAM, CBTi-YOLOv5s, Helmet Detection, Transformer, YOLOv5