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A system for detecting foreign objects on the underside of cars using a neural network

Image analysis

Basic information


ISS - Intelligent Security Systems - company-integrator of security solutions
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Software for test benches of a car - the underside is inspected by a person using mirrors and photographs
Find abnormal objects at the bottom of the car using a photo

Where this can be used:

at military sites

Why it is needed:

at border posts
in places with increased safety requirements
automate the verification process
speed up the operator's work

How it works

The car arrives at the stand
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The car's underside is photographed
When anomalies are found, it shows the snapshot to the operator
The neural network analyzes the snapshot

Technical implementation

Conducted ML audit

Since such tasks were almost not solvable, our client ordered an ML-audit service

Found a scientific article

As a result of the research, we found a scientific article on a similar topic.


We reproduced the work detailed in the article in order to adapt our own data to the model


Using our VisionHub machine models we implemented our visualization interface
Модель данных
Хостинг моделей машинного зрения VisionHub


The main method is generative adversarial networks (GAN), a machine learning algorithm.
Principle of operation:
Step 01
Remember how an object looks like in its normal state
Step 02
Generate images and capture anomalies on them
Объекты в нормальном состоянии
Зафиксирован аномальный объект
Anomaly detected

Development process

Metric - the ratio of correctly identified anomalies to false ones

Anomaly: yes / no on this fragment. There was no task to accurately localize.

Input data

images of the underside of cars with anomalies
reference images of the bottom
Very small dataset, insufficient for high-quality neural network training
High resolution images - 4K by 10K pixels

Dataset processing

We manually marked the images, noted the anomalies.
images with anomaly
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We cut the photos into 512-pixel squares and looked at them separately.

Model training

Trained the model on a prepared sample.

The accuracy of the quality metric was 0.73

Development stages

Start of development
1.5-2 months
2 weeks

Current results

The audit stage has ended
Managed to build the first model
Getting ready for a big project

Project team

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Mikhail Evdokimov
Tech lead
Vlad Vinogradov
Computer Vision Engineer
Aelita Shaikhutdinova

Features of the project

First used GAN in production
One of our first audits
This is the first time we work with an integrator company and, in general, with people who are very indirectly related to the development.

Where groundwork can come in handy

For example, in medical applications
In systems where deviation from the norm needs to be detected

Difficulties of the project

Unusual material
Communication difficulties, since the company is very large
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