Vehicle Damage Detection based on MD R-CNN

Abstract

The traditional vehicle damage assessment process is complicated and time-consuming, asking for intelligent methods for detecting vehicle damage. At present, most damage detection methods for vehicles require two models to detect damage and component where the damage is located separately, which is complex and inefficient. To this end, we propose an end-to-end multi-detection model named MD R-CNN, which simultaneously outputs damage detection and component recognition results by adding an extra classification branch. To improve the positioning precision of detection, the regression of the detection box adopts a self-attention convolution head (SA-Head) composed of a residual module and two SC Attention modules; Moreover, since there are few damage annotation datasets available, D-FPN is proposed to enhance the multi-scale detection performance. The experimental results show that MD R-CNN increases the Average precision (AP) by about 2.4% on vehicle damage datasets, and the precision can reach 80.22%, which has a favorable performance.