4 Successful Use Cases of
Computer Vision in Manufacturing
Transform your manufacturing business with insights and intelligence powered by data integration, the Industrial Internet of Things (IIoT), machine learning, and predictive analytics.
Identifying the scratches and irregularities in the painted surfaces and spare parts.
One of the crucial steps in the production of a car in the automotive industry is quality control. The vast bulk of the automobile's parts is made in other locations. To meet the quality requirements set by automakers, outside parts providers expose their component parts to in-depth testing and inspection audits. As a result, the manufacturing facilities may expect that the goods that reach their receiving ports are defect-free.
Paint faults identified using computer vision
Insufficient paint : Reduced gloss level
Missing paint: Paint missing from some areas
Droplet: Excess dried and cracked paint
Efflorescence: Contaminant preventing correct adherence of paint
Paint dust: Dried paint dust on surface
Burn mark: Reduced gloss due to overheating
Barring: Shade variation by uneven heating
Spots: Localized shade variation
Shade variation: Incorrect pre-heating of slate
According to research by the Grocery Manufacturers Association and the Food Marketing Institute, the average cost of a recall for food firms is around $10 million in direct expenditures, not including lost sales and brand harm.
Controlling the quality and safety of food and food products in contemporary food production facilities is a crucial and vital issue since the producers must closely adhere to the regulations and meet the demands of the consumers.
Quality check using Shape, color, and texture
Vegetables One of the first factors that determine a vegetable's quality is its look, which includes its color, size, shape, and shine.
Meat There are two key characteristics for predicting quality: appearance and texture. The characteristics of cooked meat pinkness, skin colour, and visual flaws all have an impact on the choice.
Bakery Products The majority of bakery goods, including muffins, bread, crackers, and cake, have specific form and size characteristics that reflect their quality. Important factors to take into account are the standard deviation of change in
Foreign Object Detection
One of the biggest causes of food recalls and customer rejection is foreign item contamination. Customers are harmed by this kind of contamination, which also results in a decline in brand loyalty and high recall costs. For instance, insects, glass, metal, or rubber are examples of foreign items. During any stage of food production.
Another significant use of computer vision in the food sector is automated visual verification of fill level and package labelling. Additionally, a visual system can determine whether a product is still fresh by using a particular ink that changes color over time and at a variable rate depending on the temperature.
By 2025, nearly half of global healthcare companies will have implemented artificial intelligence strategies, and some experts believe it will be critical for how businesses operate in the future.
Counting the number of pills in each bottle
Verifying that each pill is the right size, and form, and free from any damage
checking the package for defects or quality control
quality assurance for auxiliary products like instruction booklets
Validating barcodes and product information on labels
Pharmaceutical Bottle-packaging Detection
Pharmaceutical glass bottles are packaged and detected using computer vision technology. Automation of packaging line detection systems is becoming more and more dependent on machine learning techniques. Real-time detection and extensive quality control at numerous places are made possible. The purpose of automated quality analysis is to increase the quality and precision of fault identification in the control process, which is essential to pharmaceutical production processes.
Automated Visual Inspection for Blister Packaging
The recommended security standard for pharmaceutical blister packaging includes examining the tablets within each blister before it is sealed. The colour, size, and shape of individual tablets, as well as missing and broken ones, may all be seen using an automated visual inspection system. A quick and automated way of monitoring medication production quality is provided by intelligent detection based on ML technology and a deep learning model for seeing blister nonuniformity, spotting tablet placements within blisters, and spotting tablet colour.
PPE Complace & Work Place Safety Detection
Industrial and manufacturing are two of the industries with the highest risk of injury to employees, but this does not exclude the implementation of measures to ensure employee safety while on the job. You'll be far more likely to prevent accidents and the long-term issues they imply for both workers and their employers if AI is used to enforce compliance with all applicable environment, health, and safety (EHS) requirements.
Detecting Proper Industrial PPE Usage with Computer Vision
Despite the advantages of wearing PPE, your managers and staff are just human. How can you make sure that they are always utilising PPE correctly? The solution is found in AI and computer vision models that can quickly evaluate video footage to find safety headgear, glasses, and vests (and alarm when none are found).
Safety helmets, hard hats, and other caps
Workers are shielded from head injuries caused by crashes, falling objects, flying objects, burns, and electrical shocks with safety headgear.
Safety glasses and various types of eye protection
Workers are shielded by safety glasses from dangers like flying debris, heat, light, and radiation.
Vests for safety and other reflective clothing
Safety vests increase workers' visibility, making them more noticeable to passersby and shielding them from unintentional accidents from