Automates tracking and reporting of traffic movement in container cleaning yards for better operational efficiency.

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Computer Vision AI driven automation of tank/container tracking for cleaning yards

Using advanced deep learning techniques, DataFindr-CCY identifies and extracts data from images of containers under almost all settings — all weather and all times. It processes the extracted data for better container lifecycle management within a facility.

DataFindr-CCY easily connects to the existing IT infrastructure of the Container Cleaning Yard. Images of incoming and outgoing tanks/containers at the facilities and bays are grabbed by a cloud

based DataFindr-CCY using Computer Vision or Computer Machine Reading technologies. Images are then analyzed using AI powered techniques such as Convolutional Neural Networks (CNN) and providing intelligence that is vital for revenue enhancement, container lifecycle management and cost optimization.

How DataFindr-CCY works


DataFindr-CCY will connect to your existing IT infrastructure and continually fetch images of the positions of a tank/container anywhere in a facility.

Data extraction

DataFindr-CCY’s recognition engine extracts data from the images.

Data processing

It also processes the data to understand and analyse the passage of the tank/container through the yard.


It then passes on the data to your ERP systems using secure APIs.

How does DataFindr-CCY achieve near-100% accuracy?

Multiple algorithms for region-of-interest detection

There are multiple algorithms working simultaneously on region-of-interest identification thus ensuring 100% detection.

Adaptive field-of-view

Before extraction, DataFindr accommodates inconsistencies in data placement, such as printed matter being slightly outside the box or line.


De-noising systems from simple Otsu methods to deep learning-based segmentation algorithms improve DataFindr’s extraction quality.

Dual algorithmic journeys for building quorum

It builds quorum using two or more algorithms for every field. Each algorithm has different deep learning bases and maths for feature extraction methods, number of layers, loss functions etc.

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