Capabilities: What objects we detect

Traditional video analytics and machine vision solutions work well with standard objects and clear backgrounds. For example, if we deal with a machinery part at a clear production line or if there’s a separately standing person – standard detectors allow reliably extracting these objects, counting them and doing other operation. But if this person just sits down, standard detector can’t classify such an object any more. Same situation if there’re overlapping objects moving around. Here BitRefine Heads comes to scene.

BitRefine Heads utilizes power of neural networks that comprehend visual information not as a rule-based detector, but similar to a living brain. For example, standard optical character recognition (OCR) may not recognize all the digits on a label, if it is partly deformed. At the same time, human inspector may still be able to read all the digits. And if human can read them, then we can be sure that ANN-based algorithm at BitRefine Heads is also capable to read them.

BitRefine Heads excels at exploring complex scenes.

We work at the domains where traditional detectors don’t work. Here're typical examples:

General objects

Our platform allows detecting general everyday objects captured from any angle, located in any environment, overlapping each other.

Low quality images

Perfect source images allow using simple detectors. But the worse quality of the image, the more complex algorithms required to classify objects. BitRefine Heads works is able to effectively process sources of low quality.

Aerial images

Cars, houses, roads, trees, lakes – these objects can be recognized by neural networks. The aim of such system is usually to collect statistics from large areas.


Microscopic imagery, MRI, ultrasonography, endoscopy – all these diagnostic methods provide image that are successfully analyzed by artificial neural networks.

Inspection of materials

Neural networks are perfect for recognizing and classifying defects, like scratches, wrinkles, corrosion, cracks, welding defects, etc. Typical applications are inspections of pipelines, solar panels, at textile production, metalworking.

Different spectrum images

BitRefine Heads works equally well with images captured with regular cameras and images captured in different spectrum, such as thermal images or X-Ray images. Separate neural modules, trained on different image sets are loaded it into the BitRefine platform’s “head”.

Animal or plant species

Neural network can distinguish birch from oak as good as human can. It also distinguishes perfect fruit from all others or detect defects on a fruit.


Proven ability of deep learning to detect person despite of angles of view or body position allows tracking sportsman and collect statistics during the game.


Special group of neural network architectures are capable to return coordinates or even shape of detected object in addition to its class.

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Traditional Analytics

Traditional analytics relies on formal object specification that includes measurable values: dimensions in pixels, aspect ratios, angles, colors and histograms. This means that the environment needs to be clear, parameters of detected objects need to be well documented and lie within defined thresholds.

If you’re dealing with well described parts, passing in front of the camera always at the same orientation and same light condition you don’t need to give up traditional computer vision approaches in favor of modern deep learning. Traditional algorithms work better and much faster than anything else in this case.

BitRefine Heads

If environment is too dynamic, if object shapes are too complex, if objects are randomly orientated – solution based on artificial neural network is the right choice. Artificial neural networks along are way more powerful than standard detectors and offer recognition solutions in the areas unreachable to traditional analytics.

The advantage of BitRefine Heads platform is that it fuses traditional methods with advanced capabilities of artificial neural networks. Platform offers numerous modules that can be inserted into the video processing pipeline during setup. Some of the modules are based on neural network algorithms, others belong to traditional filters or detectors. User is free to configure video processing pipeline his own way: to include and exclude modules, to combine and reposition them – to configure BitRefine Heads according to his specific needs.

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Explanation: How BitRefine Heads works

BitRefine Heads platform is based on video processing pipeline and reporting tools. Similar to the pathways in human’s brain visual data goes from image capturing first through primary visual complex. Then it gets into main deep neural network that looks for objects of interest. Then it’s passed to the post-processor that modifies results according to the user’s requirements. Finally, data goes to external systems, to local DB, and can be analyzed through our reporting tool. Each stage of the pipeline is adjusted through the web interface.


BitRefine Heads platform is modular and can scale in line with our customers’ growing business needs. The modular structure of BitRefine Heads licenses allows customers to license and pay only for the functionality and capacity needed at given time. There are four core tenets that are important to understand when licensing BitRefine Heads software:

Video Recognition Platform: Key Features

Today business-centric video processing has become the norm and video recognition software is used now more widely within organizations around the world. Multipurpose platform BitRefine Heads offers a number of key features that help clients solve their individual business use cases very quickly. Here we present the most significant elements of the platform.

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