Companies throughout the world are exploring ways to take advantage of AI power to improve their performance, reduce the costs of running their businesses, and build positive company profiles.
Computer vision appears to be a real game changer in the field of manufacturing and infrastructure inspection, giving quality control experts and engineering professionals a powerful tool to detect anomalies, both on the assembly line and on the location of utility infrastructure.
How can computer vision be used to the fullest in the optimization of processes and enhancing the quality of products?
Defect Detection In Production Chains
One of the greatest challenges that manufacturers need to face is the occurrence of defects, which may lead to various consequences.
In the case of a defect in shoe soles, the result would probably not cause death, while potential defects on wind turbines can have serious repercussions.
AI algorithms analyze the products to ensure their quality through a thorough process of high-resolution images reporting the manufacturing process.
Any anomalies, faults, and defects, such as cracks, scratches, or misshapen components, are identified based on the images uploaded into the database.
Due to the whole process, consumers receive flawless products that are ready to use.
The capabilities of computer vision are really impressive, with high accuracy that exceeds the possibilities achieved by human-quality controllers.
The impact such perfection has on the quality of the final product and the reduction of defects, waste, and claims is well worth the effort.
One of the greatest features related to computer vision is Machine Learning Experiment Tracking, which is responsible for safely saving the information connected with all the experiments and processes run by a business.
The possibility of constant access to archive data is crucial in the optimization of the production process, as a new model does not need to be designed from scratch but can simply be downloaded from the database.
ML experiment tracking is especially important for defect detection, as researchers can easily identify factors that would affect a model’s performance, compare results, and select the optimal version.
Also, it can be treated as a way of demonstrating compliance with regulations and model fairness.
Visual Inspection of Utility Infrastructure
Proper functioning of utility infrastructure requires either big funds or a lot of time, with the need to visit all the plants for inspection.
The smooth operation and safety of power grids, wind turbines, and telecommunication networks can be achieved in a much more effective way, saving time and money.
Business owners should try using computer vision to get the burden down of them, and to delegate the tasks that can be easily performed by a robot, not necessarily humans.
In the world of modern advancements, it seems more than natural to use drones, helicopters, and ground-based cameras to monitor the infrastructure by capturing high-resolution images.
By comparing the images uploaded to the database with the captured ones, the early signs of damage can be tracked, preventing safety hazards, costly repairs, and serious blackouts.
Quality Control Computer Vision Benefits
The transfer of responsibility for quality control checks from humans to AI computer vision may bring several advantages that can be greatly appreciated by business owners and customers buying the products of the given company.
Using Artificial Intelligence (AI) and its ability to perform computer vision quality control smoothly and efficiently is one of the most significant advantages of this process.
The accuracy and consistency of AI-powered quality control should not be overlooked, as in many industries, product perfection is crucial to ensure the highest level of safety for not only the customers who purchase the product but also for everyone in their vicinity. Wind turbines and power plants are just two examples.
The maintenance of utility facilities, especially those that may pose a risk to the engineers trying to track cracks, faults, and other anomalies, can be greatly boosted by the use of computer vision.
A drone flying over the utility to take high-resolution pictures before thorough analysis by computer vision tools can be greatly appreciated.
Human intervention, which despite its expertise, may be prone to errors, is reduced to a minimum.
As a result, the responsibility for the maintenance taken off the inspectors gives them time to focus on other, more important tasks that require human operation.
To experience all of the benefits, it is recommended to find the right AI tool. Picsellia, based in France and founded in 2019, is a good example of a vision AI tool.
It is a platform offering a unique end-to-end MLOps tool for computer vision use cases, including data management and experiment tracking to deployment, monitoring, and pipeline orchestration.
