Smarter, safer, faster: AI transforms vehicle inspections
Artificial intelligence can help inspect vehicles for damage, says AI researcher Robin van Ruitenbeek.
Van Ruitenbeek's research focuses on the automation of visual vehicle inspections. The inefficient, subjective and error-prone nature of manual inspections is a growing social and scientific problem, especially in a mobility landscape that increasingly relies on sharing concepts, rental and online delivery services. The frequent transfer of vehicles between drivers requires repeated inspections, resulting in unnecessary delays and potential conflict over damage. This research aims to develop a fully autonomous vehicle inspection system based on artificial intelligence. Research has been conducted into both the artificial intelligence for detecting damage and the development of the autonomous camera system that is able to inspect moving vehicles. The goal is to significantly improve the accuracy, speed and reliability of these crucial inspections.
This research demonstrates the effective automation of vehicle damage inspections through the use of AI and advanced hardware. The results show that AI models, trained on a large dataset of vehicle damage, can achieve a detection quality comparable to human experts. The applied training methodology plays a significant role in the final inspection quality. In addition, Van Ruitenbeek and his colleagues present a new algorithm that enables vehicle inspections from multiple camera positions, which significantly improves the reliability and completeness of damage detection. Furthermore, the crucial influence of camera placement on the quality of the automated inspection was investigated. By means of an evolutionary algorithm and simulations, the optimal configuration of camera positions for the inspection system was calculated. Finally, the researchers introduce an accurate method to determine the real-time location of vehicles down to centimeter level. This makes it possible to automatically and consistently capture footage of passing vehicles, regardless of their speed or model.
The findings have significant implications for various sectors. For car rental companies and car sharing providers, automated damage inspection means faster handovers, fewer discussions about damage with customers and more efficient handling of insurance claims. For example, a rental car can be fully automatically checked for new damage within seconds after its return, so that the next customer does not have to wait and unjustified costs from the previous renter are passed on. For the logistics sector too, with the growing flow of parcel deliveries, automating inspections of vans at every shift change can increase efficiency and make responsibility for damage clearer. In addition, insurance companies can benefit from more objective and detailed damage assessments. Van Ruitenbeek's research ties in with the current situation of the growing sharing economy and the search for more efficient and sustainable mobility solutions.
The research is strongly data-driven. In order to develop and train the AI models, an extensive collection of photos of damaged vehicles was analyzed. The development of the AI models made intensive use of cloud computing to enable fast and diverse experiments. Through field research, the results of the automatic AI detection were compared with manual inspections by damage experts. In addition, computer simulations played an important role in the development of algorithms and the visualization of results, with the scientists frequently using 3D models of cars. Finally, Lensor's facilities allowed them to test solutions on a large scale, including the development of prototypes to validate the quality of our findings.
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