Portfolio

Mar 26

ROAD ASSET MANAGEMENT SYSTEM

By blank Portfolio, Portfolio slaideris sākumlapa ENG

ROAD ASSET MANAGEMENT SYSTEM

Project background:

The client has several databases containing different types of data and lacks an up-to-date inventory and management system mapping the main national roads and their adjacent assets.

Biggest challenges: To configure a mobile data acquisition vehicle with sensors, connect them to navigation equipment and synchronise them with business management system. The project requires highly accurate data, yet navigation system is subject to biases and variability in the data.

Solution:

Developed an AI system that can automatically perform a full analysis of the road and its assets by analyzing data collected on the road.

A specialized vehicle equipped with sensors, video data streams, and navigation data is used to combine these elements for automated analysis, without human intervention.

A mobile device – car – is specially equipped with necessary sensors to collect the data. Systemreceives video streams from different sensors as input, combines them with navigation data and performs automated analysis of the road and adjacent objects, by locating and classifying certain clustered objects in the image. The precise location of each object can be extracted. The solution can perform fully automated data analysis and information extraction using an AI component without requiring manual intervention.

System consists of several modules – System App, Data Collection App, Data Upload App, AI Module or Data Processing Module. The AI module performs full analysis of road and road asset data, carrying out road asset information segmentation, tracking, object classification, precise object location determination, orthophoto and 360-degree view generation.

To achieve this, solution incorporates a set of complex computer vision algorithms, as well as various machine learning solutions and models, all of which are uniquely designed to address this problem. Several custom developed and trained neural network models are used for the solution, utilizing data sets and customized neural network structures.

Key benefits:

Improved accounting, management, and related business process administration system and data reliability;
Simplified system management;
Ensuring data accuracy and information availability.

Mar 26

Love0

SMART SELF-SERVICE CHECKOUT

By blank Portfolio

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SMART SELF-SERVICE CHECKOUT

Project background:

The objective was to develop a self-service checkout by using a computer vision solutions in terms to improve customer service, make existing processes more efficient and make daily taskseasier for employees. This technology allows to automate product recognition, reducing the need for manual input and speeding up the checkout process.

Biggest challenges: the variety of the food and its appearance, different placements of the food on the plate (stacked on top of each other, covered with sauce, etc.).

Solution:

Developed a self-service checkout system that recognises food by visual appearance. The system recognises food placed on a tray below the system screen, generates a shopping list, exchanges data with the POS database to obtain a price, generates a shopping list and displays it on the screen. The system is integrated with the payment process and customer loyalty card acceptance call.

The system also includes a user-friendly UI for the checkout screen, a backend, and a cashier/administrator view for solving different situations.

Computer vision and machine learning solutions are built into the system and tailored specifically for this task. Visual data processing is real-time.

Key benefits:

Modernised customer service;
Optimised workforce and efficiency.

Mar 26

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CHICKEN EGG PRODUCTION ACCOUNTING AND QUALITY CONTROL SOLUTION

By blank Portfolio

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CHICKEN EGG PRODUCTION ACCOUNTING AND QUALITY CONTROL SOLUTION

Project background:

The objective was to develop AI solutions to replace existing mechanical solutions. Firstly, to perform online egg counting from each hen house and determine egg quality (by shape, color, shell structure, and deviations from quality criteria). Secondly, during production egg-cracking equipment could not determine when the machinery needed improvement, specifically when the cracking knives needed to be sharpened or replaced which affected the quality of the final product.

Biggest challenges: products have similar visual characteristics, which makes classification difficult.

Solution:

Production monitoring system has been developed and implemented to automate the collection of data on the amount and quality of eggs produced per hen house per day. This enables the assessment of production volumes and hen health status. The system also monitors quality of theproduced eggs by analysing the quality of the egg whites and yolks, which is used to determine whether the technical equipment needs improvement.

The system works in real-time. Computer vision and machine learning solutions have been developed and adapted to perform two different tasks – egg quality and counting control and quality control of cracked eggs. The data is collected from a real environment – production site, and is integrated with the company’s accounting system. The system works in a containerised environment.

Key benefits:

Improved production process, reduced production line downtime;
Optimised costs for data acquisition and processing;
Accelerated extraction and processing of quality data;
Real-time tracking and remote monitoring.

ROAD ASSET MANAGEMENT SYSTEM

ROAD ASSET MANAGEMENT SYSTEM

SMART SELF-SERVICE CHECKOUT

SMART SELF-SERVICE CHECKOUT

CHICKEN EGG PRODUCTION ACCOUNTING AND QUALITY CONTROL SOLUTION

CHICKEN EGG PRODUCTION ACCOUNTING AND QUALITY CONTROL SOLUTION

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