Comparative Analysis of YOLO and Faster R-CNN for Helmet Detection in Video Surveillance System - Sujadi, Karen Prakasiwi; Widodo, Triyogatama Wahyu (IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia., 2026)
The increasing number of traffic violations involving motorcyclists not wearing helmets highlights the need for an automated helmet detection system. This research aims to compare the performance of two deep learning-based object detection models, YOLOv8 and Faster R-CNN, for identifying helmet use in video surveillance environments. The dataset was collected from Roboflow and annotated into five object classes. Both models were trained and tested using Google Colab with NVIDIA Tesla T4 GPU. Evaluation was conducted using Precision, Recall, F1-Score, mean Average Precision (mAP), Matthews Correlation Coefficient (MCC), and confidence metrics. YOLOv8 achieved higher mAP and inference speed, with 74.1% mAP@0.5 and 21.80 FPS. In contrast, Faster R-CNN demonstrated better classification consistency, achieving 73.3% precision and an MCC of 0.6537. Robustness tests showed that both models were sensitive to lighting and distortion variations. In real-time video inference, YOLOv8 delivered better performance with faster latency and more stable confidence scores. The findings suggest that YOLOv8 is more suitable for real-time deployment, while Faster R-CNN offers more reliable classification under controlled conditions.