Top 5 Car Damage Detection Solutions

· Published: · Last updated: ·

Carsharing, Shared Mobility, Technology


Accidents happen and vehicles are damaged in every sharing fleet. Some damages remain undetected for a long time, entailing costly repairs and jeopardizing customers’ safety. Fortunately, there exist damage detection solutions to help fleet operators stay up-to-date on their vehicles’ statuses and keep them in good running and repaired condition. Below we evaluate five different car damage detection techniques and solutions. From traditional low-tech solutions to state-of-the-art automated damage detection, this article describes each one’s pros and cons and the type of operations they’re best suited for.

Car Damage Scratch

Key Takeaway

There’s a wide variety of car damage detection solutions. Their benefits generally correlate to their costs; the more expensive products provide the fastest and most reliable damage detection.

It might seem like new and sophisticated products have overtaken traditional solutions such as visual inspections. This is mostly true, but there are still business cases where traditional methods are a good fit due to their low cost and easy implementation. However, these solutions often cannot compete with automatic damage detection solutions that use artificial intelligence. These new and innovative products are impressive for their reliability and speed. Trained on heaps of data, AI models routinely outperform humans when it comes to identifying damages in images. Going even further, the most sophisticated solutions analyse acceleration data during trips to detect damages in real-time, notifying operators of accidents in their fleet immediately.

Relying on customers to report damages voluntarily

End-of-rental reports from customers are the most basic way of finding damages to vehicles. Businesses have to rely on their customers’ honesty and willingness to notify them of damages. This makes them vulnerable to customers claiming damages as pre-existing, making it hard to conclusively prove when damages happened. Without the customers’ cooperation, it can become difficult to find out who is at fault. In many cases, operators will be the ones to have to foot the bill.

This approach to damage detection will probably end up costing operators a lot in the long run. It might therefore be smart to invest in a more sophisticated damage detection product.

Who should use it?

Relying on customer self-reports for damage detection can be a suitable solution for very small businesses, that do not want to invest too much upfront. Also, operators that need to break into a market quickly could use this as a band aid fix until proper damage detection is setup. Finally, companies that provide an in-house fleet to their employees and can rely on their users to report damages honestly might find self-reporting satisfactory. However, while this traditional solution is inexpensive initially, it will increase running costs in the long term.


  • Light on financial and personnel resources
  • Low integration cost
  • Customers can return vehicles at any time


  • Customers often do not report damages
  • Customers may blame damages on prior rentals
  • Damages to most critical parts can go unnoticed
  • Cannot verify time of accidents
  • No conclusive assignment of culpability

Manual inspections by employees

Manual inspections require an employee to visually inspect vehicles at the end of every rental. This method of damage checks is popular because of its simplicity, but does not scale well. It requires an employee to be available for every rental completed, which limits its viability to roundtrip and station-based sharing. Furthermore, visual inspections fail to detect damages in 90% of cases. So, although this solution seems economical at first, it cannot account for all damages to vehicles. Ultimately, operators will probably still have to pay for repairs, especially on their vehicles’ underbodies where damage is more difficult to spot.

Who should use it?

Manual inspections are a good fit for operators that have diligent, frequently available employees with the right expertise. Ideally, business locations are of small to medium size and offer roundtrip rentals. This solution is also attractive to enterprises that rarely encounter damages to vehicles’ underbodies or axles and are indifferent towards minor damages.


  • Employees reliably report damages they find
  • If done diligently, inspections can clearly attribute damages to specific rentals
  • Damages can be shown to customers immediately


  • Personnel-intensive, thus costly
  • Customers can only return vehicles during business hours
  • Unreliable in detecting all damages
  • Most critical parts of the vehicle are not checked
  • Only viable for roundtrip and station-based sharing

Damage detection using customer-created images

Making customers take images or videos of their vehicle and analyzing them with AI can be quite efficient at detecting damages. Any sharing business can apply this solution, independent of whether it’s free-floating, station-based, or a roundtrip model. However, user-created images are not always of good quality. A lengthy end-of-rental handover leads to a worse customer experience, especially when customers are in a rush, for example at the airport. Customers simply do not have an incentive to be diligent when taking images. Also, AI often can’t detect damages on photos due to bad image quality. Finally, this system can’t check for damage to vehicle parts that are not easily visible.

Who should use it?

Analysis of images from customers fits frugal businesses that are looking for a compromise between saving money and still using automated damage detection. If the businesses rarely encounter underbody damages and value flexibility when it comes to returning vehicles, this way of identifying damages might suit them. It’s especially useful if businesses want customers to download their app.


  • Useful for all sharing models
  • AI image detection can spot even small damages, given good image quality
  • Constant improvement through machine learning
  • Images remain available for manual review
  • No additional hardware required


  • Time-intensive for customers
  • Only as good as image quality
  • Most critical parts of the vehicle are not checked
  • Customers may obscure damages in images

Finding damages with camera arches and AI

Several companies  provide sophisticated scanning equipment in the form of scanning arches. Operators or customers drive the vehicles through these arches at the beginning and end of every rental. The arches create ideal lighting conditions and use numerous high-grade cameras to photograph the car all around in seconds. The images are then fed into an AI-based damage detection algorithm that can reliably detect even the smallest scratches and dents. The system compares detected damages to the results of the last scan to determine if they happened during the rental. However, most products of this type do not scan the underbody of the car, although some providers offer camera and lighting equipment for this purpose as an add-on.

While this solution is dependable when it comes to creating high-quality images and detecting damages, the scanning arches can also create congestion during peak hours since every vehicle has to pass through them at low speed. Additionally, operators need to rent or buy the expensive equipment for every single business location, which can get quite costly.

Who should use it?

Analyzing automatically created images with an AI model is a suitable solution for large, established enterprises because acquiring or renting a scanning arch for every location is expensive. Preferably, operators would want fewer, high-volume locations to reduce the number of equipment needed. However, this creates a bottleneck since every vehicle needs to be driven through the scanner at the beginning and end of every rental. This makes it a good fit for businesses that can sacrifice some efficiency in exchange for the value of high-quality damage detection.


  • Fully automated 360° images at beginning and end of every rental
  • AI image analysis and high-quality images reliably find damages
  • Almost seamless handover for customers, even outside of business hours
  • Light on personnel resources


  • Only for round-trip and station-based sharing
  • Expensive scanning hardware must be rented
  • Time of damage and culprit cannot be identified
  • Most solutions do not capture the underbody
  • If customers disregard instructions, damage detection suffers
  • Scanning arch creates a bottleneck

Using AI to analyze data for real-time damage detection

The most sophisticated solutions to reliably detect damages use sensor data instead of images. A sensor box installed in vehicles continuously collects acceleration data and applies an algorithm to look for suspicious patterns. When the solution detects a possible accident, it forwards the data to servers running an AI model trained on billions of data points from thousands of rentals. The software rapidly scans the data to confirm the findings. If the AI identifies damage, it creates an information-rich accident report and notifies operators immediately.

Staying up-to-date on damages to vehicles in real-time is invaluable. Inspections at the start and end of rentals can identify damages that happened during the trip but not the exact time and severity of the accident. Customers afraid of paying for repairs might abuse this fact and deny having caused any damage. In the worst case, operators will be unable to prove them wrong and will end up having to foot the bill.

What sets this solution apart is that it provides the richest data while being the least invasive to the user experience. Customers do not need to jump through any hoops during handovers, while operators get notifications about any damages caused during the rental. It also allows operators to setup automatic workflows to keep their fleet well-maintained. The AI can distinguish between critical and cosmetic damages, so they know when to send out a maintenance team. With real-time notifications, operators can send timely help when it is needed, something that customers will surely appreciate.

Who should use it?

AI damage detection is a fitting solution for operators aiming to keep their fleet in perfect condition and maintain vehicle safety. Since it identifies damages in real-time, this method is excellent in keeping them up to date on their vehicles’ status. It is also suitable for large-scale businesses looking to improve their customer experience with seamless handovers, while avoiding legal battles over costly repairs. Because it detects damages automatically and instantly, this solution is highly scalable. Employees do not waste their time on inconclusive inspections, but instead they can focus on vehicles already identified as damaged.


  • Accurate damage detection on all parts of the vehicle, easily visible or not
  • Precise timestamps for accidents
  • Saves personnel resources
  • Permanent damage monitoring instead of end-of-rental snapshots
  • Rich information makes for easier claims
  • Automatic workflows based on real-time damage notifications


  • Individual installation required for each vehicle
  • Depending on the country of operation, customers must sign a data processing agreement
  • Provides no photographic evidence of damages

Wrap up

The list above can help operators choose a damage detection solution that fits their fleet. Alternately, they can combine multiple products to cover all their bases. For example, automatic image scans in combination with data-based AI damage detection would provide real-time damage notifications, photographic evidence, and efficient handovers.

Ultimately, the optimal solution depends very much on individual factors, such as the size of operations, the type of sharing model, or plans for expansion. Operators should consider all these factors to find the best overall damage detection solution.

Related Posts

Webinar recap: transitioning motor pools to keyless vehicle sharing

Carsharing, Technology

Webinar Recap: Transitioning Motor Pools to Keyless Vehicle Sharing

Christina Swank, Associate Director of Administrative Services at Jewish Family Service of Atlantic County, joined Paul Hirsch, CEO of Launch Mobility, and Chris Anderson, our Sales and Partner Manager for lessons learned from transitioning motor pools to keyless vehicle sharing. Keep reading for insights on signs that a motor pool is ready for a keyless system, the benefits of a digital solution, and how to plan the transition.

Free-floating Carsharing in Poland: Interview with Adam Jedrzejewski

Carsharing, Expert Interviews, Shared Mobility

Free-Floating Carsharing in Poland: An Insights Interview

The free-floating carsharing market in Poland is one of the Top 3 European markets. We asked Adam Jędrzejewski, Founder & CEO at the Polish shared mobility association Mobilne Miasto, about the first six years of carsharing in Poland, user characteristics in Polish carsharing, the key market statistics and about the leading operators in the country.

Free-Floating carsharing in Spain: Interview with David Bartolome

Carsharing, Expert Interviews, Shared Mobility

Free-Floating Carsharing in Spain: An Insights Interview

The free-floating carsharing market in Spain is one of the Top 5 European markets. We asked David Bartolome, President at the Spanish carsharing organization AVCE & Business Development and Public Affairs Manager at Free2move, about the history, why Madrid stands out, milestones, and trends of free-floating carsharing in Spain.