Car Damage Detection Based on Multi-View Fusion and Alignment: Dataset and Method

Abstract

Traffic accidents remain a significant concern due to their potential severity and impact on society. The rapid and accurate detection of car damage is increasingly crucial. Manual assessment of car damage usually relies on multi-view car images taken at the scene, which can provide richer information for damage assessment. However, most car damage algorithms are based on single-view datasets, and it is hard to fully leverage the complementary information and alignment information between distant view and close-up images. In this paper, we propose the Multi-View Car Damage Detection model (MVA-CDD), comprising three key modules: Feature Split (FS), Feature Fusion (FF), and Image Alignment (IA). The FS module extracts global and detailed information from distant-view and close-up images separately, which are then combined by the FF module. The IA module effectively aligns car damage information in distant-view and close-up images to correct errors and biases. Meanwhile, we created the new Car Damage Detection Multi-view dataset (CDDM), which has a significant advantage in both image quantity and diversity across categories, addressing the shortcomings of existing multi-view datasets. Our proposed MVA-CDD outperforms the state-of-the-art single-view and multi-view models with the dataset. Results from ablation studies further confirm the efficiency of MVA-CDD. This study contributes to optimizing the car damage detection and claims adjudication process, leading to significant labor and material cost savings. CDDM dataset is available at https://github.com/SCUT-CCNL/CDDM.