Car insurance companies lose millions of dollars each year due to claims leakage as car accidents continue to rise every day. Claims leakage is the difference between the best possible settlement and the one that is actually reached.
For a long time, car damage assessment has been done through visual inspection and validation to reduce claims leakage. However, visual inspection has its downsides which include time-consuming, leading to delays in the processing of claims and susceptibility to errors, resulting in inaccurate cost estimations. AI has the ability to quickly assess vehicle damage and the auto insurance industry can greatly benefit from this.
Recent advancements in ML, AI and computer vision, which include adopting quick, scalable and trainable end-to-end convolutional neural networks, have made it technically feasible to conduct automatic car damage recognition using convolutional neural networks. Using machine learning and artificial intelligence technology, it is possible to establish car damage detection dataset for car damage assessment deep learning.
The Role of AI in Car Damage Detection and Classification
It is typical for machine learning to be used to automate tedious and time-consuming and repetitive tasks. Machine learning based workflows allow the detection of damaged parts, and will analyze damages, predict the necessary repair and estimate overall costs. This is achievable through the use of Image/Video Annotation for Computer Vision to train machine learning models.
The use of ML models allows for the collection, analysis and dissemination of insights, which ultimately leads to expedited inspection procedures that take into more accurate consideration factors such as the road, the illumination, the weather, the amount of traffic, the speed, the type of damage and the accident severity.
How to Construct a Robust AI Car Damage Detection and Training Dataset
To consistently and accurately conduct car damage detection and classification even on the tiniest of scuffs and scratches in a range of situations, such as those involving water and dirt, car damage assessment deep learning requires analyzing tens of thousands of high-quality images of damaged cars to equip computer systems with enough knowledge. Therefore, high-quality training data must first be gathered, followed by car damage annotation and data segmentation.
Initially, machines learn from the data you provide them. The machine learning model you employ needs access to accurate data to identify patterns. The quality of the data you give your model determines its accuracy. Inaccurate or out-of-date data can lead to erroneous outcomes, which can lead to irrelevant predictions.
To be successful, ML models for car damage detection and classification need access to a vast collection of high-quality, relevant photos and videos. The model would be improved by having more data coming from a wider variety of sources.
Some third-party providers can sell you the datasets if you do not have the necessary resources or enough time to collect your own car damage datasets. Insurers can use the off-the-shelf car damage image dataset to train ML models to assess vehicle damage accurately, minimize losses and predict insurance claims.
As soon as the data has been collected, the system should be able to automatically detect and evaluate the objects and conditions involved to estimate the exact amount of damage in the actual world. For the purpose of ML model training, this is where data annotators come in to assist with the car damage annotation of hundreds of photos and videos.
It is possible to use the annotators to highlight & to classify damages them into categories such as:
damage vs non-damaged ;
damaged Side: front, rear, back ;
severity of the damage: MINOR, MODERATE, SEVERE ;
damage Classification: scratch, broken headlamp, broken tail lamp, bumper dent, no damage etc.
How to Train ML Models to Identify Car Damage
For ML models to be trained to detect car damage, a vast number of photos and videos that have been annotated thoroughly is required. Data that has not been labeled correctly prevents machine learning models from accurately detecting damage. The data should be reviewed by both humans and tools to ensure it is of the highest possible quality.
The following three parameters should be taught to the models:
determining presence of damage or not ;
accurately pinpointing a specific area of damage ;
assessing the extent of damage based on location, its nature and whether or not it can be repaired.
To properly examine the damage to the vehicle, it is necessary to train the model to look for patterns. An ML algorithm should be used to examine and interpret the training dataset.
As advancements in AI, ML and computer vision continue, conducting car damage visual assessment and recognition will be a thing of the past. Insurance companies stand to benefit significantly when it comes to using AI and ML for car damage detection. Not only does the technology fasten the underwriting process, but it also prevents fraud. Car damage detection also benefits the likes of car repair and rental services since it brings much-required transparency to the process of calculating costs for repairs and making repairs, as well as bringing transparency between customers and rental car companies during the car rental process.