Traffic Vehicle Detection Using Faster R-CNN: A Comparative Analysis of Backbone Architectures - Hakim, Luqman; Hendrawan, Aria; Khoiriyah, Rofiatul (Asosiasi Doktor Sistem Informasi Indonesia, 2024)
Object detection is a crucial task in computer vision, where advanced deep learning models have shown significant improvements over traditional methods. In this study, the Faster R-CNN algorithm is applied to a traffic dataset containing six vehicle categories: Bus, Car, Motorcycle, Pick Up Car, Truck, and Truck Box. The novelty of the research lies in the comparison of four backbone architectures ResNet50, ResNet50V2, MobileNetV3 Large, and MobileNetV3 Large 320 evaluated for their performance in vehicle detection at IoU thresholds of 0.5 and 0.75. The results reveal that ResNet50 provided the best overall performance, achieving mAP scores of 0.966 at IoU 0.5 and 0.887 at IoU 0.75, offering a balanced trade-off between precision and recall. ResNet50V2 and MobileNetV3 Large also performed well, with mAP scores of 0.945 and 0.870 for ResNet50V2, and 0.969 and 0.843 for MobileNetV3 Large, respectively. However, MobileNetV3 Large 320 showed the lowest detection performance, with mAP scores of 0.857 at IoU 0.5 and 0.551 at IoU 0.75. These findings provide useful insights into the suitability of different architectures for vehicle detection tasks, particularly in traffic surveillance applications.