Navigating the Challenges of Implementing Computer Vision in Business

By Umang Dayal

February 29, 2024

Gartner’s 2023 Impact Radar highlights emerging technologies for leaders to improve, differentiate, remain competitive, and capitalize on market opportunities. Many of these emerging technologies are based on computer vision that's revolutionizing EdTech, healthcare, automotive, retail industries, and more. 

Implementation of computer vision technology in your business can be an expensive and challenging task, that requires expert supervision and strong data annotations. This blog discusses four challenges that you can face while implementing computer vision technology. We will explore a few use cases and problems associated with implementing CV, and provide recommendations so you can create a sustainable business and maximize your ROI.

What is Computer Vision?

Computer vision is more than just an image recognition technology. It provides intelligent recommendations to make decisions based on unseen images. Machine learning or software trained using these AI models can quickly process images or videos and make intelligent decisions. Computer vision can perform various functions such as image classification, segmentation, facial recognition, feature matching, extraction, pattern recognition, and object detection.

How Computer Vision is Reshaping Businesses?

Artificial intelligence and automation systems trained using computer vision are changing business operations with minimal to no human interaction. Companies such as SpaceX are using AI and automation to dock astronauts successfully in the International Space Station. Grocery stores are using automatic checkout features to buy products. 

Computer vision systems are being developed to help many industries such as healthcare, security and surveillance, transport or traffic management, and much more.

Top 4 Challenges of Implementing Computer Vision in Business

Poor Data Quality & Training 

High-quality data annotations and labeling are the foundation for any computer vision system. In the healthcare industry, it is important to use high-quality data as any repercussions due to inaccurate or incomplete data sets can significantly damage medical operations. This was witnessed during COVID-19 when a computer vision system failed due to poor quality of data sets. 

If you are planning to mitigate this issue you should consider working with medical data annotation specialists who are experts in building computer vision systems. 

For training data sets, you need sufficient and relevant sources which can pose challenges for many companies. For example, if you are working in the healthcare industry collecting data sources can be a challenge because of its sensitive nature and the privacy concerns of the patients or hospitals. Most of these medical data sets are strictly private and not shared by hospitals or healthcare professionals. This means developers might not have enough data sets to train computer vision systems to begin with. 

Solutions For Poor Data Training

To resolve this issue and obtain adequate data for your computer vision programs you should consider outsourcing or crowdsourcing your project. This reduces the overall burden of collecting data sets and the responsibility of quality management will be transferred to a third party that specializes in computer vision data gathering and data annotation services. You can work with a trusted third party to obtain and train your visual data sets for your computer vision projects.

High Costs

Any computer vision application’s architectural design and infrastructure contribute to its total cost, which can be highly variable when considering its functionality or when software or hardware is not adequate. 

A web or mobile application that only analyzes a few images is completely different from computer vision systems that are highly advanced and resource intensive and perform various tasks such as image and video processing in real time. These powerful processors, complex hardware, and software increase the costs exponentially. 

Read more: Hurdles in Autonomous Driving

Solutions To Reduce High Costs

To decrease costs use cross platforms for hardware and software requirements while processing data sets. Use pre-processed models to standardize images before feeding them into machine learning algorithms, this provides better accuracy for the training models. To increase the delivery or deployment of applications reduce the use of manual coding for applications. Instead, use automation tools which does not require too much human interference. Use up-to-date data annotation frameworks to make a big leap in real-time object detection and performance.

Weak Planning

Another challenge in implementing computer vision in business can be weak planning for machine learning models used for the deployment of a project. If executives set overly ambitious targets in the planning stage the data science team might find it difficult to achieve objectives. This can lead to unnecessary costs, insufficient accuracy, inaccurate results, or unrealistic computing power. 

Solutions To Avoid Weak Planning 

To overcome these overly ambitious targets businesses should create stronger planning by understanding and analyzing technology’s maturity levels. The executives should create measurable objectives with definitive targets. The ability to acquire data sets or purchase labeled data sets should be discussed beforehand. Before initiating the project, the planning team must consider the costs of training models and deployment. To avoid mistakes you should learn from existing case studies that are similar to your business domain. 

Read more: High-quality training data for autonomous vehicles

Inadequate Hardware

Computer vision technology is incomplete without the right combination of hardware and software. To ensure its efficiency businesses must install sensors, bots, and high-resolution cameras. These hardware components can be costly and if installed incorrectly, it can lead to blind spots making the computer vision systems ineffective.

Solutions To Avoid Inadequate Hardware

To avoid this challenge businesses should consider installing high-resolution cameras that provide adequate frames per second for the computer vision system. The engineers must cover all surveillance areas using cameras and sensors so there are no blind spots left. For example, in the case of a retail store cameras should cover all the items on each shelf. The two most significant costs during installation are the hardware requirement and costs of cloud computing which should be considered in the planning stage. All devices should be properly configured before the computer vision system is deployed. 

Final Thoughts

The computer vision implementation process is complex and requires expertise and deeper understanding from all stakeholders. To quantify your ROI, businesses should consider data quality, overall costs, hardware requirements, and stronger planning to obtain measurable results. If your project has time constraints you should consider outsourcing data collection or computer vision solutions to a third party. We at DDD can help you with computer vision services that require technical expertise and dedicated machine-learning tools.