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Top 4 Computer Vision Challenges & Solutions in 2024

Computer vision (CV) technology is revolutionizing many industries, including healthcare, retail, automotive, etc. As more companies invest in computer vision solutions, the global market is projected to multiply 9 times by 2026 to $2.4 Billion.

However, implementing computer vision in your business can be a challenging and expensive process, and improper preparation can lead to CV and AI project failure. Therefore, business managers need to be careful before initiating computer vision projects.

This article explores 4 challenges that business managers can face while implementing computer vision in their business and how they can overcome them to safeguard their investments and ensure maximum ROI. We also provide some examples in the recommendation sections

1. Poor data quality

You can work with an image data collection service to help you obtain high-quality visual datasets for your computer vision project.

Poor Quality

High-quality labeled and annotated datasets are the foundation of a successful computer vision system. In industries such as healthcare, where computer vision technology is being abundantly used, it is crucial to have high-quality data annotation, and labeling since the repercussions of inaccurate computer vision systems can be significantly damaging. For example, Many tools built to catch Covid-19 failed due to poor data quality.

Recommendations: Working with medical data annotation specialists can help mitigate this issue.

You can check our list of medical data annotation tools to choose the option that best suits your healthcare computer vision project needs.

Lack of training data

Collecting relevant and sufficient data can have various challenges. These challenges can lead to a lack of training data for computer vision systems. For example, gathering medical data is a challenge for data annotators. This is mainly due to the sensitivity and privacy aspects of healthcare data. Most medical images are either of a sensitive nature or are strictly private and are not shared by healthcare professionals and hospitals. Additionally, it is possible that the developers do not have the resources to collect sufficient data.

Recommendations: To ensure that you have adequate data to train your computer vision system, leverage outsourcing or crowdsourcing. This way, the burden of collecting data and ensuring its quality will be transferred to a third-party specialist, and you can focus on developing the computer vision model. You can also work with a video data collection service to obtain high-quality visual datasets for your CV project.

2. Inadequate hardware

Computer vision technology is implemented with a combination of software and hardware. To ensure the system’s effectiveness, a business needs to install high-resolution cameras, sensors, and bots. This hardware can be costly and, if suboptimal or improperly installed, can lead to blind spots and ineffective CV systems.

IoT-enabled sensors are also required in some CV systems; for example, a study presents the use of IoT-enabled flood monitoring sensors.


The following factors can be considered for effective CV hardware installation:

  • The cameras are high-definition and provide the required frames per second (FPS) rate
  • Cameras and sensors cover all surveillance areas
  • The positioning covers all the objects of interest. For example, in a retail store, the camera should cover all the products on the shelf.
  • All the devices are properly configured to avoid blind spots.

One good example of improper hardware for CV is Walmart’s shelf-scanning robots. Walmart recalled its shelf-scanning robots and finished the contract with the provider. Even though the CV system in the bots was working fine, the company found that customers might find them strange due to their size, and they found other more efficient ways. 

On the other hand, Walmart-owned retail brand Sam’s club mounted new CV-enabled inventory scanning systems, made by Brain Corp, on its already operating autonomous floor cleaning robots. Sam’s club finds them more effective and plans on increasing the investment.

Another example is Noisy student, which is a semi-supervised learning approach developed by Google, that relies on convolutional neural networks (CNN) and 480 million parameters. Processes like these require heavy computer processing power.

Two of the most significant costs to consider before starting your computer vision project are:

  • The hardware requirements of the project
  • The costs of cloud computing

3. Weak planning for model development

Another challenge can be weak planning for creating the ML model that is deployed for the computer vision system. During the planning stage, executives tend to set overly ambitious targets, which are hard to achieve for the data science team.

Due to this, the business model:

  • Does not meet business objectives
  • Demands unrealistic computing power
  • Becomes too costly 
  • Delivers insufficient accuracy and performance


To overcome such issues, it is important for business leaders to focus on:

  • Creating a strong project plan by analyzing the business’s technological maturity levels
  • Create a clear scope of the project with set objectives
  • The ability to gather relevant data, purchase labeled datasets or gather synthetic data
  • Consider the model training and deployment costs
  • Examining existing success stories similar to your business.

4. Time shortage

During the planning phase of a computer vision project, business managers tend to focus overly on the model development stage. They fail to consider the extra time needed for:

  • Setup, configuration, and calibration of the hardware, including cameras and sensors
  • Collecting, cleaning, and labeling data
  • Training and testing of the model

Failure to consider these tasks can create challenges and project delays

A study on companies developing AI models found that a significant number of companies have significantly exceeded the expected time for successful deployment.

Another recent study identified that 99% of computer vision project teams faced significant delays due to a multitude of reasons:


We recommend performing early calculations of each stage of the development process. If the project is time-constraint, then certain tasks, such as algorithm development or data collection, can be outsourced.

You can also check out our sortable and filterable lists of services, vendors, and tools to choose the option that best suits your business needs:

Further reading

If you have any questions about challenges in computer vision, don’t hesitate to contact us:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Shehmir Javaid
Shehmir Javaid is an industry analyst in AIMultiple. He has a background in logistics and supply chain technology research. He completed his MSc in logistics and operations management and Bachelor's in international business administration From Cardiff University UK.

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