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As a Chief Operating Officer, you want to introduce efficiencies into the operational processes, applying modern technology to improve safety and quality of work.
As a Director of Quality Control, you want to organise your processes so that you can spot damaged goods in the warehouse, identify substandard or visually incorrect components and track objects on video.
As a Safety Manager, you want to improve the accuracy of operations, detect potential hazards such as fires, gas leaks or unauthorised access in time and respond in real time. You want to have the tools to monitor the safe distance between people and equipment to prevent injury.
Computer vision is a niche area of technology that, when applied correctly, can help your organisation in solving these challenges. It helps facilitate the creation of smart factories that can automatically perform tasks normally done by humans. Advanced computer vision technologies allow us to identify objects e.g. workers in a warehouse, product defects, shell craters; analyse data from surveillance cameras and use it for safeguarding or navigation. Autonomous robot control, 3D mapping, augmented reality are quickly becoming the reality. Companies that use this technology in their operations can safely declare themselves to be part of Industry 4.0 – the highest degree of interoperability between industry and technology.
Traditional algorithms - We use various image processing techniques such as filtering, segmentation, and morphological operations to extract useful features from images; pattern recognition algorithms such as support vectors and neural networks to classify objects in images; and machine learning algorithms such as decision trees and k-nearest neighbors to make predictions and decisions.
In our projects, we use ResNet - this neural network is a highly regarded solution for image classification, face recognition, and other computer vision tasks. We also use InceptionV3 for high accuracy image classification and SSD for object detection in images.
We can train a neural network to detect defects on products such as scratches, dents, and cracks, teach it to recognize license plates on cars, or segment images to highlight specific objects or areas.