Image Recognition in Industries
Image Recognition in Manufacturing

From small startups to multinational corporations, businesses are recognizing the need for powerful data processing capabilities to remain at the forefront of their respective industries. This has pushed them to explore and adopt new technologies.

How Does AI Image Recognition improve Data Quality?

Data Quality depends on various sources of data including visual and textual data

Image recognition is a significant discovery in computer vision and a hallmark of the 21st-century global revolution. By utilizing large-scale visual datasets, machines have the power to draw insights from images and videos. This has helped organizations seek data-driven solutions.

Over the years, the functionality and speed of AI image recognition algorithms have improved. While accessibility to this cutting-edge AI software is not an issue, organizations must check the viability of integrating it into their operations. They must also consider the capabilities and benefits of every platform before arriving at a decision.

Image recognition can be impactful across industries- from the agriculture to the automotive sector. 

But, what are the other facets of computer vision that are impacting industries?

1) Object Detection

AI Platforms can identify and correctly label different objects

Object detection enables computers to locate objects in images or videos. Typically, machine learning or deep learning is used to train object detection algorithms and produce meaningful results.

This technology is particularly significant in the development of advanced driver assistance systems (ADAS), which enhance road safety by detecting driving lanes and potential obstacles.

When working with object detection using deep learning, there are two primary approaches. The first one involves training a custom object detector from scratch, which entails designing a network architecture to learn the relevant features of the objects of interest. To achieve this, organizations require a large set of labelled datato train the convolutional neural network (CNN).

The second approach involves using a pre-trained object detector and network and fine-tuning it for your specific application. This method can yield quicker results because the object detectors are already trained on thousands, or even millions, of images.

2) Image segmentation

In Image Segmentation, objects of the same category like vehicles are given the same color, compared to other objects like buildings, trees and roads.

In medical imaging, automated driving, video surveillance, and machine vision, image segmentation is used. In this process, images are converted to distinct regions of pixels that are designated by a labeled image or a mask.

Clustering separates objects in a scene into groups, while semantic segmentation is used to label each pixel. Pixel values are analyzed for abrupt discontinuities like edges.

3) Optical character recognition

Optical Character Recognition

Optical Character Recognition (OCR) is a computer vision technique used to convert images of text into machine-encoded text. OCR enables computers to recognize printed or handwritten characters and convert them into machine-readable text that can be further processed.

The OCR (Optical Character Recognition) method consists of many steps, like pre-processing the image to ease character identification, segmenting characters in the image, extracting features like shape or texture and then utilizing a trained machine learning approach for classifying each character.

It’s used to digitise paper documents, improve accessibility for visually impaired individuals, and automate data entry works. OCR technology has advanced in recent years with its algorithms achieving near-human accuracy.

Here are the 3 most popular AI image recognition industry use cases


Image Recognition in Retail helps in Customer Profiling

Many retail stores are employing AI image recognition as a best practice. This technology is capable of analyzing data to assist retailers in making informed decisions and monitoring customer behaviour.

Self-checkout systems use data from CCTV or cameras embedded in shopping carts to identify purchased items and complete checkout for the customer. Planogram image recognition models can detect and classify products sold in stalls by brand or manufacturer. Product tags on photos simplify item navigation, while visual search offers customers product recommendations based on images. Shelf image recognition software even notifies store staff if an SKU is missing and needs to be restocked.

Image recognition technology provides a wealth of benefits that streamline the retail industry and improve overall customer experience.


Dentist checking 3D model of dental implants

Research conducted at the University of Pennsylvania suggests that AI image recognition software can improve healthcare outcomes by providing more accurate and timely diagnoses of illnesses. The software can reduce false positives and negatives, thereby delivering better patient care. This could be a game-changer for the industry.

Not just that, it can also assist in lowering healthcare expenses by facilitating the identification of illnesses.

Image recognition models can be trained to process MRI and X-ray output, as well as other visual data, to detect and accurately identify any anomalies. Additionally, these models can aid physicians in developing an early treatment plan for their patients.

As well as standardizing diagnosis, the models can be deployed across many hospitals and clinics. It is also possible to reduce the stress young doctors might feel while diagnosing patients through training and human error prevention. Platforms such as Akaike offer project management functionalities to review the deployment of image recognition models, while creating a safety net for junior doctors to use the models.

Image recognition applications are now being used in dentistry to assist with the detection of problems that may Warrant invasive treatment. X-ray machines can take pictures, and using object detection models, dentists can be directed to any area they should pay extra attention to. In addition, image similarity search models help find relevant information regarding similar conditions based on a patient’s test results from a medical examination.


In manufacturing, AI image recognition software is an invaluable tool. This advanced technology detects objects, defects and other key data quickly and accurately, thus increasing production efficiency with lower downtime. Additionally, these models provide early detection of potential problems which can prove very beneficial to the quality of products. This process is commonly known as defect detection and can be used in all kinds of manufacturing facilities. Furthermore, AI object detection and image classification is particularly useful in sectors such as electronics and food manufacturing too.


In the electronics sector, product safety is critical, and faulty products can have fatal consequences. Even minor defects in electronic manufacturing can result in costly recalls, prolonged downtime, and reputational damage to the manufacturer. Due to the intricate nature of electronic components, such as memory cards, processors, and motherboards, object detection models are increasingly being used to identify and locate defects with high accuracy and speed, helping manufacturers maintain quality and efficiency standards.

Food Distribution:

Image Recognition helps in defect detection of fruits and vegetables

AI image recognition is also proving to be very useful for food distributors. Object detection models can be trained to automate the sorting process of separating good and damaged fruits and vegetables by scanning the produce as it moves along the feed and identifying and locating them according to their respective categories. In combination with custom sorting procedures, these object detection models can improve the speed and efficiency of early-stage fresh produce processing, which is crucial when working with fresh fruit and vegetables to ensure quick delivery to the shop floor.

Final Thoughts

The Grand View Research report projects that the global image recognition market will grow up to 18.8% CAGR from 2020 to 2027, indicating a high potential for AI image recognition software.

While the capabilities of this technology have grown exponentially in recent years, there is still room for improvements and advancements. Despite these challenges, it’s clear that investing in AI image recognition software could revolutionise many industries, bringing about many opportunities for growth and success. If you haven’t done so already, now is an excellent time to start leveraging this innovative technology for your business.