AI-Assisted Car Damage Detection: Methodology and Approach for High Accuracy
Updated: Feb 22
Introduction
Cars can suffer from damages when they come into contact with sharp objects, road accidents, or any other incidents that harm the car's body, impacting its original shape and form. Automated car damage detection is becoming increasingly common, and many users are turning to these sophisticated systems. This technology is improving as vehicle damage detection using AI becomes more prevalent. Many different types of car damage detection techniques are available at our disposal.
Vehicle damage detection using AI benefits auto insurance, leasing, and rental car companies. Convolutional neural networks trained on images can discern automotive damage in photographs and videos. The detection of car damage is critical for the automotive industry and its stakeholders. It detects damage and calculates repair costs. Manually inspecting every aspect of a car is typically unfeasible, however vehicle inspections are efficient and practicable when machine learning techniques are used. AI damage inspections can also recognise and assess numerous forms of damage in seconds. Users can then make more educated decisions on the state of their vehicles.
Car Damages Types
Cosmetic and structural automotive damage are the most common following an accident. Some of these problems are visible, while others may be more difficult to spot. Whatever the cause of the damage, it is critical to understand the many forms and how they can influence your vehicle. Understanding the various types of automotive damage might assist you in analysing them. Users can immediately learn about the scope and level of damage by using vehicle inspection automation.
A car can sustain a number of different types of damage. The level and scope of the damage vary depending on how the car's body comes into touch with external objects. The severity and extent of the damage will be determined by how the vehicle acquired the damage. Some faults are repairable, while others can cause enough damage to the car part that it must be replaced.
Car damages can be divided into three types based on which component they affect. Metal damage, glass damage, and miscellaneous damage are the three categories.
Metal Damage
Metal damage occurs on the metallic components of the vehicle's body. The bumper, hood, doors, dickey, and other metal sections are among these components. Metal damages are further categorised into three types: dent, scratch, and tear.
Dent : Dent is the damage caused when the metal body is forced inward when it is subjected to external pressure. The metal surface becomes concave as a result of this pressure. When an automobile crashes, a large number of dents might result.
Scratch: Scratches are the most common type of damage to metal surfaces. Scratches happen when a metallic automotive part moves or rubs against the surface of another hard or sharp object. Scratches can also emerge if a hard or sharp object travels across the metallic surface of the car. Scratch damage is depicted in the photographs below.
Tear: When a car is ripped, the metal part is torn into pieces. This tear can occur at the car part's border or within it. The images below depict rip damage.
Glass Breakage
Glass damage occurs on the glass section of the car body, just like metal damage on metal parts. It has an impact on the windscreen, back glass, automobile windows, headlight, and taillight. Glass damage is further classified as follows: crack, chip, spider crack, and large range glass damage.
Other Damage
Miscellaneous damages are those that do not come under either metal or glass damage. For example, the photographs below show a gap between automotive elements. Car parts dislocation is also included in this category.
Damage Detection
We now have a thorough understanding of the various forms of damage that automobiles can experience. Let's talk about how we can discover these damages using images of the car. Despite the fact that there are numerous sorts of damage, the approach for detecting damage is basically the same, with a few exceptions. To detect damage in a particular image, this general technique employs an object detection network trained on a large number of annotated photos.
The detection network can be trained to detect all types of damages, or we can train multiple networks to detect specific types of damages. The latter is the most effective technique. This is because different damages have distinct properties. As a result, training a single model for all damages may reduce model performance. This degradation in performance manifests as false positives or false negatives of damage detected through an image.
False positives are errors that occur when the algorithm incorrectly recognises damage in a picture where no damage occurs. For example, the AI may incorrectly identify poles, shadows, object reflections, and the surroundings as damage. False negatives are errors in which the AI is unable to detect damage despite the fact that it is present in the image. Our key goal is to train the models to detect damage and reduce the frequency of these two mistake scenarios.
The default technique, as previously stated, is to train an object detection network. However, in a few circumstances, this strategy does not work. These cases will be treated individually below, as will the methodologies used to determine damages in such cases.
Let's go over the most frequent object detection strategy. Detecting objects is a key task in the field of computer vision in Machine Learning. Its goal is to find things in a given image. Deep learning and its advancements are the major algorithms that aid in accomplishing this goal. These networks include Faster-RCNN, Retinanet, YOLO, and others.
There are numerous networks that have been published that we can use to address our problem statement. The best results will be obtained by appropriately identifying the appropriate networks. Detection architectures include networks such as Faster-RCNN and Retinanet. These networks can be altered by modifying the backbone from which the characteristics are extracted. MobileNet, VGG, Resnet, EfficientNet, and other backbones are examples. Each backbone has a unique network complexity and processing time necessary to extract the features. The correctness of each backbone varies depending on the task at hand.
Object Detection for Damage Identification
Damages such as a rupture in the metal body and dislocation or gaps between parts are easily detected by detecting networks. When trained on big and well-represented datasets, they produce accurate results. Consider the figure below, which depicts damage detection. All of the rips and dislocations have been identified here. Glass damage can also be detected using this detection paradigm. The image below, for example, demonstrates the correct identification of a spider crack.
Damage Assessment Using Ensemble Techniques and Segmentation Models
As previously stated, while some damages can be diagnosed using object detection, using the same technique for other damages such as scratches and glass cracks is insufficient. The main source of concern in such circumstances is false positives. As the problem becomes more difficult, detecting the damages becomes more difficult. Nonetheless, there are ways and strategies that can aid in the development of a successful model capable of reliably detecting such damages. Let's look at these strategies in the context of scratch detection, glass crack detection, and dent identification.
Detection of Cracks in Glass
The technique used to detect scratches can also be used to detect glass cracks. Furthermore, detecting glass breaks is more difficult than detecting scratch damage.
Damage Detection Using Tracking
It is common for us to train the model on data that is insufficiently large. This condition can occur due to a paucity of photos or a lack of labelled data. In this instance, the trained model may be insufficiently robust, resulting in a high number of false positives or false negatives. In many cases, we are given a video shot by the client, and the ML system is trained to precisely forecast the damages. In this scenario, despite the less robust model, we can precisely anticipate the damage and its position by using information from nearby frames. As a solution to this challenge, video analysis and tracking objects (here damage) are used.
Conclusion
Automated car damage detection significantly decreases human error. Furthermore, as more data is collected to train the algorithms, car inspection technology improves over time. As previously stated, ensemble approaches improve the accuracy and dependability of vehicle inspection automation.