Real-Time Instance Segmentation Models for Identification of Vehicle Parts

Abstract

Automated assessment of car damage is a major challenge in the auto repair and damage assessment industries. The domain has several application areas, ranging from car assessment companies, such as car rentals and body shops, to accidental damage assessment for car insurance companies. In vehicle assessment, the damage can take many forms, from scratches, minor dents, and major dents to missing parts. Often, the assessment area has a significant level of noise, such as dirt, grease, oil, or rush, which makes accurate identification challenging. Moreover, in the repair industry, identifying a particular part is the first step in obtaining an accurate labor and part assessment, where the presence of different car models, shapes, and sizes makes the task even more challenging for a machine-learning model to perform well. To address these challenges, this study explores and applies various instance segmentation methodologies to determine the best-performing models. This study focuses on two genres of real-time instance segmentation models, namely, SipMask and YOLACT, owing to their industrial significance. These methodologies were evaluated against a previously reported car parts dataset (DSMLR) as well as an internally curated dataset extracted from local car repair workshops. The YOLACT-based part localization and segmentation method outperformed other real-time instance mechanisms with an mAP of 66.5. For the workshop repair dataset, SipMask++ reported better accuracy for object detection with a mAP of 57.0, with outcomes for $\mathrm{A}{\mathrm{P}}^{\mathrm{I}\mathrm{o}\mathrm{U}=.50}$ and $\mathrm{A}{\mathrm{P}}^{\mathrm{I}\mathrm{o}\mathrm{U}=.75}$ reporting 72.0 and 67.0, respectively, whereas YOLACT was observed to be a better performer for $\mathrm{A}{\mathrm{P}}^s$ with 44.0 and 2.6 for object detection and segmentation categories, respectively.