(untitled) (DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE, 2025)
This paper presents a practical solution for real-time recognition of vehicle model and body color on real-world CCTV streams in Uzbekistan. The dataset was annotated in CVAT and cleaned; to ensure fair evaluation, we applied a camera-wise train/val/test split (preventing cross-camera leakage). On the same dataset, we fine-tuned YOLOv8-l, DETR (ResNet-101), and CenterNet, and analyzed the quality–speed trade-off from a deployment perspective. Using detector-produced bounding boxes as ROIs, we integrated a fast color module into the pipeline based on a combined LAB+HSV approach. For inference threshold selection, we employed a practical method grounded in the F1–Confidence curve. The system operates robustly with multi-camera RTSP streams, supports auto-reconnect and resource monitoring (CPU/RAM/IO and, when available, GPU), and is ready for ONNX/TensorRT export. The results demonstrate practical applicability in traffic monitoring, security, and intelligent video surveillance systems.