Optimized YOLOv11 Architecture for Accurate Multi-Class Vehicle Detection under Real-World Conditions - Parag Hossain; Md. Hamim Ferdous; Md. Sabbir Mahmud; Shahin Alam (Peneliti Teknologi Teknik Indonesia, 2026)
Real-time detection of vehicles is essential for modern transportation systems, traffic surveillance, and autonomous driving technologies. With recent progress in deep learning, object detection models have become more reliable across complex and dynamic environments. This study presents a YOLOv11n (nano variant, 2.59M parameters)-based system designed to accurately detect three key vehicle classes cars, buses, and trucks in real time. Unlike prior YOLO versions, YOLOv11 introduces an improved attention mechanism and anchor-free detection head specifically addressing partially occluded and multi-scale vehicles. A customized dataset (compiled from Open Images v7 and BDD100K, spanning day/night and clear/rain/snow conditions; class distribution: cars 48%, buses 30%, trucks 22%) containing 9,989 training images and 1,998 validation images was used to fine-tune the network. Data augmentation techniques, including Mosaic augmentation, HSV color transformations, and random flips, were applied to enhance model robustness. All experiments were conducted on an RTX 3060 Laptop GPU (6GB VRAM). The trained model achieved strong detection performance, with precision of 0.806, recall of 0.751, mAP50 of 0.830, and mAP50–95 of 0.674 (the 15.6% drop indicates moderate localization errors at stricter IoU thresholds). Inference speed reaches 526 FPS (inference-only) and 263 FPS end-to-end, outperforming YOLOv8n by 3.2% in mAP50. These results highlight YOLOv11n’s ability to balance accuracy and computational efficiency, making it well-suited for real-time applications on resource-limited hardware. The proposed detection framework can support future developments in intelligent traffic management, mobility analytics, and automated road monitoring systems.