VEBD-HEL: A noval approach to vehicle exterior body damage parts classification in intelligent transportation systems

Abstract

Vehicle Exterior Body Damage(VEBD) parts classification is important for claim processing, cost estimation, accident investigation, and vehicle damage assessment. Class imbalance in the VEBD part classification dataset is a primary factor affecting the classification performance of existing classification models. Although the availability of datasets and the system’s capability significantly impact system performance, its damage part classification is still limited due to its dynamic body structure, size, shape, color, and types of damage. In this paper, we propose a novel heterogeneous ensemble learning (HEL) model based on VEBD data (VEBD-HEL) to deal with imbalanced data in VEBD. We validate the effectiveness of VEBD-HEL on two original and generated VEBD datasets. The experimental results demonstrate that compared with current state-of-the-art models, VEBD-HEL has the best comprehensive performance. The proposed model not only achieves good Accuracy (99.93%) rates for both the simple damage and the severe damage but also increases the Area Under Curve (AUC) to 99.83%.

Keywords

• Deep learning
• Machine learning
• Classification
• Heterogeneous ensemble learning
• Bayesian optimization