What is Car Damage Recognition?
Image recognition, or computer vision, is the machine or computer’s ability to detect an object, feature, or valuable information from an image or a sequence of images (such as video). In the automotive industry, the first process of detecting vehicle damage is based on visual observation of the aesthetic state of the car body and its components. However, the process of manually identifying, counting, and assessing damage in a vehicle is not trivial. For this reason, the application of new technologies such as computer vision systems is reshaping the future of car damage recognition procedures.
As a society, we are facing a new era of industrialisation and technological advances that can reshape how humans and machines interact. Automation and artificial intelligence (AI) have a crucial role in the continuous development, innovation, and creation of new technologies across several industries, such as the automotive sector.
The development of AI systems aims to provide machines with the cognitive functionalities and capabilities to perform and complete complex tasks that humans typically perform. These new technologies increase the accuracy and response times of the systems while mitigating risks and avoiding human error.
What is key to the success and the future outlook of image recognition?
When considering the application of artificial intelligence and machine learning in the automotive industry, our first point of reference is the self-driving technologies or the multiple industrial robots working and collaborating in production lines and manufacturing vehicle components. However, these are not the only applications of AI in the industry. Computer vision and deep learning technologies shape innovations in visual scanning, damage detection, and vehicle assessment applications.
How do you create a successful image recognition process?
As a general overview, the essential component of a vehicle damage scanner is an array of high-resolution machine vision cameras capable of scanning from different angles and viewpoints the entire vehicle body structure to generate a computational representation of the vehicle and its damage. Then the machine learning algorithms and computer vision technologies effectively detect and classify the types of damage on the vehicle with the additional functionality of extracting additional data such as the length of the scratches or the size of a dent. This information is accurately validated and presented in a ready-to-use report to automate the quoting and cost of repair estimation.
What is the difference between a traditional process and a vehicle damage assessment system based on a scanning solution?
In the vehicle insurance industry, the traditional claiming process depends primarily on people and their visual observations, from inputting and evaluating the data to the repair cost estimation. This process is time-consuming and may be affected by human error or by the intention of fraud. In the traditional method, the claim process requires several inefficient steps where multiple staff members are involved, making it less than optimal for the insurance companies to proceed with the damage repair stages.
With the implementation of vehicle damage scanners based on computer vision technologies and artificial intelligence, the damage evaluation is carefully assessed by the image recognition model.
The use of these technologies reduces the common mistakes and intuitive factors that might occur during manual estimation to provide high accuracy and trustful results. The application of computer vision drastically minimises the cost of firstly the employee, as well as claim handling processes, and potential repair overpayment. This brings further benefit in that the employee would be released from the clerical, repetitive, and tedious work to focus on more exciting tasks where their human expertise and knowledge are required. Automating such a process with the help of machine learning and remote sensing would make the process a lot more convenient for both sides of the damage, increasing the productivity of the insurance provider and the customer’s satisfaction.
A continuous learning model
An additional benefit of the application of machine learning is the optimisation of the system and its continuous learning. The damage recognition models can be updated, allowing the predictions to consider new tasks, damage types, and data. With these functionalities, the next generation of vehicle damage scanners will re-use and retain previous knowledge while learning from new data to ensure continuous system improvement over time. The process of optimisation is seemingly unstoppable.
Image: Vehicle Appearance Component Recognition Algorithms.
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Who benefits from Car Damage Recognition, and how?
As a result of the development of these technologies, the automotive industry leaders will have in their hands highly accurate systems capable of assessing the aesthetic and integrity condition of a vehicle, including the detection of bumper damage, dents, windscreen shatters, broken headlights, and scratches in real-time. AI-driven applications will provide predictive insights that allow for fast analysis and vehicle scanning capabilities. The collected data can be used to automate the damage assessment process and to calculate the repair costs in minutes.
Vehicle scanning technologies can add value to insurance firms, rental car companies and general vehicle repair centres with cutting edge technologies and computer vision capabilities allowing for agile, efficient, and adaptive results.
This could be of value for repair shops, car resellers, or other intermediaries where the model output could facilitate informed decision-making, accurate pricing, and more efficient staff planning. We can be sure that in the automotive industry, AI-driven applications are here to stay and provide businesses with the necessary tool to focus on their area of expertise and allow us, as final customers with a better user experience, reliable results and reduced waiting times.
The main benefits from vehicle damage assessment system based on scanning solution:
Car damage recognition can make your life easier
The concept of extracting and making sense of data from images provided brought the main benefits: timely and accurate information, streamlined process, high processing capacity. As a result, car damage recognition enables shorter processing time, image fraud detection, and higher accuracy in detection and estimation – thus less human intervention.
- Prevents fraud, speeds up in times the underwriting process.
- Decreases operational costs, brings customer satisfaction and higher retention rate.
- Creates a collaborative environment, brings transparency to the repair process and costs.
- Enables staff to focus on their real area of expertise.
- Reliability on the damage assessment results for the customers, insurance companies, and vehicle repair centres.
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