Visual-based vehicle detection with adaptive oversampling

Abstract In this study, the class imbalance issue in vehicle detection was addressed. Specifically, certain classes such as Tow Truck were found to have significantly fewer samples compared to others such as normal trucks. This imbalance could be adversely impacted algorithm performance, favouring abundant classes over underrepresented ones. After thorough analysis, an adaptive dataset augmentation approach was proposed for the underrepresented classes. Evaluation was first performed on classic and state-of-the-art object detection methods. All experiments were undertaken on a tiny dataset called Multimedia University Diversity Dataset (MMUVD). The fastest training process and the highest mean average precision (mAP), which stood at 0.686 for mAP50 and 0.439 for mAP50-95, were demonstrated by You Only Look Once version 8 nano (YOLOv8n). By applying adaptive oversampling to the dataset and retesting it again on YOLOv8n, mAP50 was improved to 0.950 and mAP50-95 to 0.717, respectively. Notably, the contribution lay in identifying the optimal detection algorithm for vehicle detection, and the proposed adaptive oversampling method ensured consistent performance across all classes, enhancing the overall accuracy and reliability of the system.