Automatic Car Damage Assessment System: Reading and Understanding Videos as Professional Insurance Inspectors
Author(s) -
Wei Zhang,
Yuan Cheng,
Xin Guo,
Qingpei Guo,
Jian Wang,
Qing Wang,
Chen Jiang,
Meng Wang,
F. R. Xu,
Wei Chu
Publication year - 2020
Publication title -
proceedings of the aaai conference on artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 2374-3468
pISSN - 2159-5399
DOI - 10.1609/aaai.v34i09.7110
Subject(s) - damages , computer science , upload , segmentation , reading (process) , computer security , mobile device , artificial intelligence , human–computer interaction , world wide web , political science , law
We demonstrate a car damage assessment system in car insurance field based on artificial intelligence techniques, which can exempt insurance inspectors from checking cars on site and help people without professional knowledge to evaluate car damages when accidents happen. Unlike existing approaches, we utilize videos instead of photos to interact with users to make the whole procedure as simple as possible. We adopt object and video detection and segmentation techniques in computer vision, and take advantage of multiple frames extracted from videos to achieve high damage recognition accuracy. The system uploads video streams captured by mobile devices, recognizes car damage on the cloud asynchronously and then returns damaged components and repair costs to users. The system evaluates car damages and returns results automatically and effectively in seconds, which reduces laboratory costs and decreases insurance claim time significantly.
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