Comparative Analysis of Data Augmentation Strategies Based on YOLOv12 and MCDM for Sustainable Mobility Safety: Multi-Model Ensemble Approach

The transportation sector is an important stakeholder in greenhouse gas emissions. Sustainable transportation systems come to the forefront against this problem, with the solutions within the scope of micro-mobility especially attracting attention for their environmentally friendly structures. While micro-mobility vehicles reduce the carbon footprint in transportation, their widespread use remains limited due to various security concerns. In this paper, an image processing-based process was carried out on vehicle and safety equipment usage to provide solutions to the security concerns of micro-mobility users. The effectiveness of frequently used data augmentation techniques was also examined to detect the presence of micro-mobility users and equipment usage with higher accuracy. In this direction, two different datasets (D1_Micro-mobility and D2_Helmet detection) and a total of 46 models were established and the effects of data augmentation techniques on YOLOv12 model performance outputs were evaluated with Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), one of the Multi-Criteria Decision-Making (MCDM) methods. In addition, the determination of Multiple Model Ensemble (MME), consisting of multiple data augmentation techniques, was also carried out through the K-means clustering–Elbow method. For D1_Micro-mobility datasets, it is observed that MME improves the model performance by 19.7% in F1-Score and 18.54% in mAP performance metric. For D2_Helmet detection datasets, it is observed that MME improves the model performance by 2.36% only in the Precision metric. The results show that, in general, data augmentation techniques increase model performance in a multidimensional manner.