Image recognition revolutionizes many business sectors, from retail to agriculture. It is actively spreading across the globe. By 2030, the technology market is expected to become a USD 134 billion industry, transforming many sectors as we know them today. The growing adoption of machine learning and artificial intelligence is a key factor fueling this trend.

Yet, image recognition has its problems that continuously drive engineers, including the AI/ML team at CHI Software, to enhance its precision and reliability. Today, we are ready to share what we have learned from our experience.

This article will uncover the barriers we faced and touch on common issues in the field (with solutions to solve them). Join us on this informative journey to the world of image recognition!

Image Recognition for Preschool Reporting: Real-Life Insights

First, let us explain our experience in AI image recognition based on the solution we built for education. 

Project Background

Many parents in Japan rely on preschool services for their toddlers during weekdays, usually from early morning to afternoon. Naturally, mothers and fathers want to stay connected and keep an eye on their kids’ activities and well-being. To meet this need, parents typically receive reports for emergencies and attendance. 

Our client decided to move further and add individual photo reports to give parents the most detailed glimpse into their children’s preschool life. 

Our Image Recognition Solution

The client’s idea was to build a mobile app that uses AI image recognition to identify and tag each toddler in photos from a camera or a gallery. Teachers only need to send this visual report to parents – fast and simple.

CHI Software’s development team was involved in this project from the outset, and that is what we came up with in four months:

• Registering new students: Teachers can add a child’s info to the app by taking their photo or picking one from the gallery and adding their name and surname;
• A list of restricted students: The app contains a list of children whose parents have not given consent to photography. It alerts teachers when such students appear in a photo;
• Photo processing: Teachers can snap a photo or select one from the gallery and instantly see all the children labeled;
• Sent photos count: The app tracks and displays the number of photos sent to each parent;
• Cancellation option: Teachers can cancel sending a photo if it does not have any students they need to report on.

It Takes a Village to Identify a Child: Project Challenges 

Every image recognition project has its barriers to overcome, and ours is no exception. But we believe these issues only make our work more fascinating. 

Local Project Logic

The unique feature of our app is that it runs on a device without any back-end server. So we had to carefully choose a neural network capable of running locally while still being effective. After testing ten different options, we finally found the one that met our project’s specific requirements.

Image Recognition in Complex Environments

Teachers capture photos of children during their daily activities, so our app had to recognize each child regardless of the angle, environment, or lighting conditions. Therefore, we had to consider all potential issues that could prevent our app from identifying the kids correctly.

Real-time Image Recognition Difficulties

Our app must recognize faces instantly, which is a tough task even for complex models. But in the end, we succeeded in developing a system able to identify up to five faces simultaneously in just 0.75 seconds.

Precision in Image Detection

Privacy was a big concern for our client. Our app needed to accurately determine which parents should get photos and be extra careful about not taking photos of kids who should not be photographed. We had a goal of 85% accuracy and reliability, and we are proud to say we met it.

Would you like to learn more about the tech stack we used and other exciting info? All of it is waiting for you in our case. 

The 7 Common Image Recognition Problems and How to Solve Them

Our case has just shown how captivating and, at the same time, challenging image recognition can be. Now let us explore further what issues you might face when looking to develop a similar app. We will examine the most common barriers of image recognition systems and effective strategies for overcoming them.

Poor Lighting 

Changes in brightness, shadows, and dark spots can impact the ability of algorithms to recognize objects in images. Image normalization can help deal with this problem.

Occlusion

When objects are overlapping or partially blocked, it can confuse image recognition algorithms that rely on seeing the whole object. Enhanced computer vision models that can infer the full object from partial views can become a solution.

Perspective Variations

Objects seen from different angles or perspectives can be difficult to identify. Data augmentation during training can expose algorithms to more viewpoints.

Scale Variation

Fluctuations in object sizes due to camera proximity impact the ability to detect and classify objects. Multi-scale processing helps improve the work of object detection algorithms.

Clutter 

Busy backgrounds full of objects can make it hard to pinpoint and recognize the main subject of an image. Image segmentation is used to help algorithms to “understand” the picture and separate objects.

Dataset Bias

Dataset bias occurs when data for model training inaccurately represents the diversity of the real-world environment. It happens due to underrepresentation or overrepresentation of certain groups or characteristics within the data, leading to poor results. Careful dataset curation is a go-to practice to overcome this issue and provide the required system efficiency.

Adversarial Attacks

An adversarial attack is the process of making small, undetectable changes to an image that confuse a machine learning model but go unnoticed by the human eye. These attacks can cause major issues in important areas such as self-driving cars and identity verification systems. Building strong, resilient models and enhancing them with adversarial training are the best strategies for addressing the issue.

The Last Glance

Image recognition technologies are turning once wild and unbelievable concepts into reality. Although this domain is evolving rapidly, it’s important to be aware of the challenges you might encounter. Typical problems include difficulties in image processing, navigating complex environments, and meeting stringent requirements for speed and accuracy. 

These challenges demand a skilled hand to solve. So if you have visionary ideas and need technical assistance to realize them, you are in luck to find us.

CHI Software is more than a team of skilled AI/ML engineers; we are a community that breaks all barriers by bringing innovations to the table. How about pushing the limits together? Just send us a message to get started.

FAQs

• What technologies are used for image recognition?

  Image recognition relies on a combination of technologies. AI algorithms use neural networks to process and analyze vast amounts of image data. Machine learning frameworks like TensorFlow and PyTorch are used to develop and train image recognition models. Advanced hardware, like GPUs and TPUs, increase the speed and efficiency of training and deploying these models.
  

• What are the main real-life applications for image recognition?

  Image recognition aids in medical diagnosis, automates driving, streamlines retail operations, fortifies security systems, improves agricultural yields, assures manufacturing quality, sorts social media images, and powers visual searches in apps, making everyday tasks more efficient and accurate.

• What are the main image processing challenges?

  In image processing, key challenges include dealing with image quality and object image variations, achieving real-time analysis, and ensuring high-level accuracy across complex conditions.

• What are the key image recognition system limitations?

  Image recognition systems have difficulties with context interpretation, occlusion, and varying viewpoints. Real-time processing becomes an issue, too, as it requires heavy computational resources. One should also remember about adversarial attacks and concerns regarding potential biases in training data.

• How accurate are advanced image recognition algorithms?

  Advanced image recognition algorithms have made significant progress in their precision but still have not reached the level of human vision. The top image recognition models can achieve over 90% accuracy in ideal conditions. For complex image recognition tasks like detecting multiple objects or segmenting images, an accuracy rate of around 80-85% is considered a success.