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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
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:
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:
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.
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:
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:
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.
Share article
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
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:
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:
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.
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:
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:
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.
