How to use image recognition to detect damaged products
16 Jul 2025
3 min read
It’s terrible when a customer has a damaged or defective product. It can also be time-consuming for agents to address. There can be a back and forth to get customers to share the right images, and then an agent has to review the images to check whether a refund is due.
For users searching for the right image recognition solution, it's important to consider the available APIs and tools to best fit their needs.
The good news if these issues are consuming a lot of your agents’ time is that they can be sped up and even automated entirely. Welcome to the world of Visual AI. Users can leverage various tools, APIs, and apps to automate and streamline damaged product detection workflows.
How to speed up “Damaged Product” workflows
As a platform building AI Agents for ecommerce businesses we talk a lot about resolving customer issues. While there is obvious value in fully automating a customer service interaction, there can be a lot of value in just partially automating it. Here’s why:
If a customer has an issue with a damaged product and wants a refund, then a customer service agent needs some information in order to action this. This could be images or videos of the damage, proof of purchase, warranty information, serial numbers, and proof of identity. With text detection, agents can extract serial numbers or warranty information directly from images, while face detection can be used for verifying identity. Additionally, analyzing video submissions allows for a more comprehensive damage assessment.
Then there might need to be a follow up because one of the images is blurry, or the customer has missed one of the crucial pieces of information, or so on.
If you fire off an email one evening about a damaged product, the agent may ask for all of this information the next day while you are in the office without access to all this information. Suddenly this simple request has lasted days – the customer is starting to get impatient, and it’s no one’s fault.
Now, imagine that instead of having to wait for an agent to come online to ask for all of this, you had an automated response that would ask for all the necessary information. Suddenly you’ve eliminated that lengthy back and forth, and the whole process is massively sped up.
The goal here is to ensure that the customer service agent has everything at her fingertips to quickly assess the situation and then close things off. If you can validate, using AI, that the files the customer has sent are valid and appropriate then you can save even more time. Tracking features can also be used to monitor the movement or condition of products in submitted videos or images, further enhancing automation and validation.
But what about using AI the whole way through. This is where Visual AI really comes into its own. These systems can describe the content of images and videos, helping agents make faster and more accurate decisions.
Using Visual AI for Image Recognition
Using the power of AI platforms’ ability to recognise images, now you can even check the images that customers send for defects or damage. By uploading pictures of common issues with your products you can train an AI Agent to be able to recognise those issues when they occur. You can also create a custom model tailored to your specific products and use cases, ensuring the AI Agent is optimized for your business needs. The model can be trained to detect different types of objects and defects in photos submitted by customers, making it highly versatile for various scenarios.
This means that your AI Agent can already make a call about whether a refund or replacement is needed, or some other action is required. You could even fully automate this and have the AI process it immediately.
Similarly you could use it to check serial numbers are valid, or that the item itself is authentic based on certain brand specifications. This can involve using text detection as a feature and labeling images for training, so the AI learns to identify and verify the correct information.
Not all “damaged” items require replacement. Sometimes the customer needs to be educated on how to care for their product. Let’s give an example.
Online florists and plant suppliers need customers to put their products in water straight away but sometimes the plant doesn’t seem to come to life. Analyzing photos and videos can help detect and describe the condition of the product, such as identifying wilted leaves or signs of mold.
Time is a crucial factor here, so it’s important that customer service teams are able to jump on these issues quickly. Visual AI can detect the plant and then be trained to give the appropriate advice considering the plant, the condition it is in, and how long it has been since the plant arrived.
If it has only been 24 hours, the advice might be to wait another day or so. If there are signs of damage or mold, then the brand can ship some replacements or offer a refund. And so on.
There are many real-world examples and use cases of image recognition for damaged product detection across industries. Here is our case study with Bloom & Wild which explains this exact use case.
To understand how machine learning works in this context: the AI model is trained on labeled photos and examples of different types of objects and defects. Through repeated analysis and feedback, the model learns to accurately detect and classify issues, making it a powerful feature for any image recognition project.
Benefits and Challenges of Image Recognition for Damaged Product Detection
Image recognition technology, powered by advances in computer vision and artificial intelligence, is rapidly changing how businesses detect damaged products. By using image recognition apps and platforms, companies can analyze digital images and videos of products—whether on shelves, in warehouses, or submitted by customers—to identify defects and damage in real time. This shift is delivering significant benefits across industries, from retail to manufacturing.
One of the standout advantages of image recognition for damaged product detection is its ability to process and analyze multiple images at scale, far faster and more accurately than manual inspection. For example, retailers can use image recognition applications to scan shelf images and instantly flag damaged or defective items, ensuring only quality products reach customers. This not only streamlines operations but also boosts customer satisfaction by reducing the chances of a damaged product making it into a customer’s hands.
Image recognition technology also reduces the manual labor required for quality control. Automated image analysis means staff can focus on higher-value tasks, while the system handles repetitive inspection work. For businesses, this translates to lower operational costs and more efficient workflows. Additionally, the actionable insights generated by image recognition models can help businesses identify recurring issues, optimize their supply chain, and improve overall product quality.
However, implementing image recognition for damaged product detection does come with challenges. High-quality, well-labeled training data is essential for building accurate custom models, and gathering this data can be both time-consuming and expensive. Environmental factors—such as lighting, camera angles, and image resolution—can also impact the accuracy of image classification and object detection, sometimes leading to false positives or missed defects.
To address these challenges, businesses are increasingly turning to custom vision solutions offered by platforms like Google Cloud and Amazon Rekognition. These services allow companies to train models on their own product images, creating custom models tailored to their unique needs. Transfer learning is another powerful tool, enabling businesses to build on pre-trained models and adapt them to new data with less effort and cost. Cloud-based APIs and machine learning frameworks like TensorFlow and PyTorch further simplify the process, offering scalable, web-based solutions that can be integrated into existing business operations.
Continuous improvement is key to maintaining high performance in image recognition applications. Models need to be regularly updated and retrained with new data to stay accurate as products, packaging, and environmental conditions change. Cloud-based services make this process more manageable, providing access to the latest advancements in artificial intelligence and computer vision without the need for extensive in-house expertise.
When it comes to pricing, image recognition technology offers flexible options. Cloud providers like Google Cloud and Amazon Rekognition offer pay-per-use APIs, free tiers for smaller projects, and scalable pricing to fit different business needs. The cost of developing custom models depends on factors such as dataset size, model complexity, and the resources required for data annotation and training. By carefully evaluating these factors, businesses can choose the right combination of features, performance, and cost for their specific use case.
How to get started with Visual AI
To get started, you will need enough images to train a model in order to successfully detect products and defects. If you sell something that is generic, such as a plant or flower, then you may be able to find data from elsewhere. If everything is very branded, then you may need to go back through your tickets and look for images that customers have sent you. It's important to be able to manage large collections of media, such as images and videos, for effective training and analysis.
If you have a cache of data, we can help to assess if it’s enough to be confident about Visual AI’s ability to detect faults. A computer vision platform can help you hit the ground running with pre-built features and easy integration, making it easier to start analyzing your media content right away.
When selecting a solution for your business, be sure to consider the price and available pricing options, as different platforms may offer various prices and tiers based on your needs.
So if you want to start automating for damaged and defective products, speak to us today.
