The role of computer vision in AI-powered inspections:

Computer vision is the backbone behind all recent developments in image processing. With the popularity of deep learning, and techniques like GANs (generative adversarial networks) and attention mechanisms (Transformers), the ability of machines to understand, interpret and analyse visual data has grown immensely.

For AI-powered vehicle inspections specifically, models are now able to detect every defect, down to the smallest crack. This is possible because of the versatility of computer vision.

Computer vision can be used for general interpretation of visual data as well as task-specific processing. Tasks like

image segmentation
Classification
minute object detection

Deep learning models can perform these tasks, which are essential for vehicle inspections. Computer Vision systems can also analyze steering and braking, helping insurers analyze incident recordings and assign fair compensation.

These AI-powered inspections are not only highly accurate but also immensely faster than manual methods. They require no supervision and have no inherent biases. As deep learning algorithms become more prevalent in industry products, having a well-trained image analysis algorithm like Inspektlabs will soon become the norm for inspections.

Evolution of Computer vision for vehicle inspections

Computer Vision has existed as a field of study since the early 1980s, with researchers using computational techniques to digitally process images. However, even with the brilliant advancements, AI-based image processing has made it to mainstream applications fairly recently. There is a long and fascinating history behind how image processing has reached the level it is currently at.

Early days – basic image processing:

In the 1980s and 1990s, the earliest attempts at automating image processing involved rule-based algorithms.

Computer Vision as a concept was first developed in the 1980s, with the development of edge detection and feature extraction algorithms. During this time, most basic object detection was through filters like Sobel and Hough Transforms and the Canny edge detector.

The Hough transform was a formative discovery for the first object detection algorithms. But these primitive algorithms were only capable of detecting lines, edges, and other geometric shapes.

These systems relied on rule-based processing of images, where pixel values were computed and manipulated using filters like Sobel, Prewitt, and Canny edge for boundary detections.

While these computations were advanced for their time, they lacked adaptability. The ability of these techniques was limited by several factors, like lighting conditions, viewing angles, and surface reflectivity.

The Machine Learning Shift:

The limitations of rule-based computational systems led researchers to explore supervised machine learning in the 2000s. Researchers began developing machine learning (ML) algorithms specifically for computer vision. One such algorithm, developed in 2001, the Viola-Jones face detection algorithm, was one of the most significant breakthroughs in image processing.

These algorithms allowed computers to learn from data and improve their accuracy by iterative training methods. Some vital discoveries from this time were

Support vector machines (SVMs) and random forest classification algorithms, which enabled binary and multi-class classification of images.
Feature descriptors like HOG, SIFT, and SURF that were manually extracted from images to feed into classifiers

However, these models still required expert feature engineering to work well and could not adapt to varied, real-world data.

CNNs and the Deep Learning revolution

Today’s deep learning revolution started all the way back in 2012, when AlexNet, a GPU-based convolutional neural network, won the ImageNet challenge.

ImageNet was a dataset comprising 12 million images across 22000 categories. The paper detailing this dataset was published in 2009, which then led to the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). In the challenge, competitors had to correctly classify images and detect objects across a trimmed list of 1000 ImageNet categories. The models that produced the lowest error late won the challenge.

ISLVRC became the primary benchmark to evaluate the performance of computer vision models. And, in 2012, AlexNet blew its competitors out of the water. (https://mmcalumni.ca/blog/understanding-the-inception-of-artificial-intelligence-unveiling-the-origins-and-evolution)

Before AlexNet, an architecture consisting of deep convolutional layers was considered unlikely to succeed, mainly due to the high computational load. CNNs were initially overlooked because of the large amounts of data required to train them. Smaller implementations failed to produce good results, and deep networks were considered computationally unrealistic.

What made AlexNet successful was a combination of factors at just the right time. Nvidia’s release of CUDA just a few years ago allowed access to parallel processing on GPUs, making the deep convolutional model computationally realistic.

CNNs eliminated the need for manual feature extraction by learning visual hierarchies directly from raw pixel data. Architectures like the VGG (visual geometry group) model series introduced a uniform stack of 3×3 convolutional filters for depth and simplicity.

ResNets further accelerated progress by adding skip connections to deep convolutional networks, which helped deal with the pressing issue of gradient vanishing.

On this basis, many models were developed and released in the subsequent years. Some of the highlights included

– YOLO (you only look once)

– Faster R-CNN

– Shift-Invariant U-Nets

– Vision Transformer

These innovations were the basis for the large-scale deployment of automated vehicle inspection systems in production environments.

AI-Powered Inspection Workflow

AI-powered vehicle inspections are incredibly convenient, not just for the insurer but also for the policyholder attempting a claim. The insurer can easily compare the pre-inspection and claim inspections and generate reports detailing the repair costs. The policyholder can be assured of fair premium and cost calculations. All these features make this AI integration extremely beneficial.

There are several steps involved in the Inspektlabs vehicle inspection workflow:

https://www.inspektlabs.com/insurance

1. Image acquisition & pre-processing

The first step in accurate inspections is image collection and preprocessing. The workflow is nearly identical for both pre-inspections and damage claim inspections.

The vehicle owner is asked to capture 360-degree views of their vehicle, along with clear images of all external and internal parts. That data is then uploaded to a secure cloud storage.

These images and videos are then processed by the image preprocessing pipeline, which accepts or rejects the images. This step evaluates the quality of the images uploaded and may request the owner to take photos that are better illuminated or cover more angles.

The usual preprocessing techniques include denoising, colour correction, normalization, and background removal to prepare the data for the model’s consumption.

Once the preprocessing model is satisfied with the quantity and quality of media uploaded, it begins inspecting it using segmentation and detection models.

2. Damage detection & classification

The approved images of the vehicle are then passed onto the damage detection models, which identify the problem area, assess damage costs in case of a claim, and generate a report with damage categories.

The damage detection process employs ensemble models, including detection models that detect damage areas and segmentation models that isolate minute damages by assigning labels to each pixel.

Inspektlabs’ ensemble model is trained using semi-supervised learning. Semi-supervised learning helps the model understand the task through a small number of labelled images, which can then be generalised to the bulk of unlabelled training data.

3. Damage severity estimation & report generation

In case of claim inspections, the workflow has classification models that identify the severity of the damage and classify the claim based on the cost of repair/replacement as well as the cause of the damage.

The system then generates a detailed report including all relevant information, starting with details of vehicle condition, repair costs, price of replacement parts, and labour costs. It also specifies old damage from its records that isn’t covered by insurance repairs.

As a final step, the fraud detection system analyses all elements of the claim, investigating potential fraud patterns and false claim filings, and corroborating the damages claimed with the incident report.

The report is then sent to the claim adjuster for a final estimate, which the insurer will approve or reject.

Challenges and Ongoing Improvements in Computer Vision for Vehicle Inspections

While AI-powered models reduce the cost and time overhead for vehicle inspections, several challenges that the models face in everyday operations may cause delays or faulty claim processing.

Handling rare damages

One of the more persistent challenges for inspections is the accurate identification and classification of rare or atypical damage types. These may include undercarriage rust, hail dents, electrical fire burns, or subtle signs of tampering—each of which occurs infrequently in general datasets. Due to their scarcity, these damages are underrepresented during training, which makes it difficult for deep learning models to generalize or even recognize them during inference.

In the few cases where the damage to the vehicle is unusual or undetectable, the model may identify false positives.

GANs can be used to generate synthetic data to resolve this issue. Generative adversarial networks create real representations of damage types by learning from a small dataset of labelled samples. This generated data can be used to augment the dataset and improve the robustness of the model.

However, this option isn’t foolproof as synthetic data could present unrealistic possibilities and warped images that would affect the accuracy.

Environmental variability

Outdoor environments introduce several factors that can interfere with accurate image-based inspections. Shadows cast by nearby objects, glare from sunlight, accumulation of dirt on the vehicle, and adverse weather conditions like rain or snow can distort the visual characteristics of a vehicle’s surface.

These inconsistencies introduce noise into the image data, which lowers model performance during the inference process compared to the training phase.

This variability can be controlled by preprocessing and by removing any background unrelated to the vehicle itself that may cause uncertainty to the model.

Ensuring consistency across all vehicle types

Vehicle inspections must account for a wide range of vehicle body types, sizes, and surface geometries. From compact hatchbacks to large SUVs and commercial trucks, the physical differences can challenge standard computer vision models. These models may struggle to generalize because damaged features, such as dents or scratches, can appear differently depending on surface curvature and camera angle.

Transfer learning is often integrated into the training process of the model to improve adaptability to diverse vehicle types. Pretrained models on large-scale datasets are fine-tuned on diverse vehicle images, helping the model retain a general understanding while adapting to new vehicles.

Future Trends

There is a huge scope for advancements in AI inspections, particularly in internal and structural damage assessment. The latest breakthroughs in image processing like self attention mechanisms can be adapted to this use-case to better generalize the models.

3D damage assessment

The transition from 2-D to 3-D damage analysis marks a significant leap in vehicle inspection accuracy. While 2D images provide limited spatial context, 3D techniques allow systems to interpret surface deformation depth, volume, and spatial orientation—factors critical for accurate damage severity estimation.

Modern 3D reconstruction relies on several technologies:

Stereo vision systems compute depth by comparing pixel disparities between two or more synchronized camera views.
LiDAR (Light Detection and Ranging) uses pulsed laser light to produce high-resolution point clouds, enabling precise modeling of surface damage, such as dents or gouges.
Structure-from-Motion (SfM) are algorithms that reconstruct 3-D structures from multiple 2D images captured at various viewpoints. This is very useful in mobile inspections where a complete picture of the vehicle is not available.

Integration with autonomous vehicle ecosystems

Autonomous vehicles have become widespread in the auto industry, especially with the popularity of Tesla self-driving cars.

Embedding this AI-driven inspection systems into their operational infrastructure would make it much easier to conduct inspections on the vehicle.

When integrated into fleet management ecosystems, inspection results can initiate real-time alerts and schedule maintenance or repairs without human intervention. These inspections can also be used to collect data and feed into a linked platform tracking all vehicles under the company.

Linked platforms allow for consistent observation of vehicle conditions, which also protects fleet management companies against negligence claims.

This integration minimizes downtime, optimizes service cycles, and ensures AVs comply with safety protocols.

Multimodal AI models for improved inspection

Multimodal models are the next big thing in Artificial intelligence. With spatial understanding already being integrated into top-tier models like Gemini and ChatGPT, the natural progression seems to be to incorporate this into vehicle inspections as well.

Multimodal models support a richer context, allowing for text explanations of damages seen in the visual input. Multimodal models can also integrate audio support, with microphones that can capture sounds of collisions or mechanical anomalies.

Transformer architectures already support image and text inputs, and incorporating just these two input data types could greatly improve the accuracy of damage detection.

A recent paper by MIT engineers also discusses a potential technique to use multimodal models for fraud detection, that uses text and visual inputs along with feature engineering. This shows the scope for incorporating multiple input types into vehicle inspection workflows.

Conclusion

The evolution of computer vision from being considered unnecessary and computationally heavy to becoming indispensable in AI-based applications represents a shift in the perspective of using AI in mainstream workflows.

As vehicle companies are moving towards autonomous vehicles and AI-powered steering, the need for fast and easy inspections is increasing. Even now, AI-powered inspections have numerous benefits over manual inspecitons, and they make everyone’s lives easier.

In the coming future, AI-powered vehicle inspections will become critical infrastructure, enabling pre-emptive maintenance, smooth insurance claims, and immediate flagging for potential fraud.