From Synthetic Data to Real-World Efficiency: Automated Vehicle Damage Detection and Vehicle Parts Segmentation

The growing urbanization of modern societies intensifies the demand for sustainable mobility that aligns efficiency, safety, and environmental responsibility in smart cities. Shared vehicle systems address this need, but remain limited by subjective manual inspections that hinder operational efficiency. Automated exterior damage assessment via computer vision offers a scalable alternative, although its success critically depends on high-quality, domain-specific datasets, which remain scarce. To overcome this limitation, we introduce SYNDCAR (SYNthetic Damage CAR dataset), a generative AI-based dataset that simulates realistic vehicle damage in images with precise annotations for detection and vehicle parts segmentation. Extensive experiments were conducted that covered hyperparameter tuning, transfer learning, benchmarking of CNN- and Transformer-based models, and post-training optimization and quantization. The results highlight the challenges of damage detection due to appearance variability and dataset size, while pointing to practical solutions: YOLOv8-X achieved the highest accuracy, whereas YOLOv12-S provided the best trade-off. TensorRT deployment tests (FP16/INT8) further reduced the inference time by ≈ 71% on the GPU and the energy consumption by 65%. Segmentation remained more energy-intensive than detection. In general, SYNDCAR demonstrates the potential of synthetic datasets to enable accurate, efficient, and scalable vehicle inspection systems.