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Top 5 Computer Vision Best Practices in 2024

Top 5 Computer Vision Best Practices in 2024Top 5 Computer Vision Best Practices in 2024

Computer vision is revolutionizing every industry, including manufacturing, automotive, retail, and healthcare, by making operations faster and more efficient and achieving productivity and accuracy levels that humans can not match. However, developing and implementing a high-performing computer vision system is not easy. Computer vision systems can fail to perform or meet expectations for a variety of reasons.

This article explores 5 best practices that business leaders and developers can use to create and implement a high-performance computer vision system successfully.

1. Get the right training data

The performance of a computer vision system relies on the data that is used to train it. The better the quality of the training data, the more accurate the system will be. To achieve the maturity level that current computer vision systems have, highly accurate and consistent data labeling is required. Therefore, manual data annotation is not adequate.

Companies can either use an in-house data annotation tool or outsource the process. While using an in-house tool, the developers must consider the type of data being annotated before choosing the tool. For example, a medical image annotation tool should have polygon labeling since medical images have irregular shapes and require precision labeling.

Outsourcing data annotation can be another effective way of ensuring quality. Data labeling and data annotation services are highly equipped with the tools and expertise required for quality results. However, the decision to outsource in-house annotation depends on the project’s requirements. To learn more about outsourcing data annotation, check out this comprehensive article.

To learn more about medical data annotation, check out our comprehensive article.

2. Provide adequate training

Lack of training is another major reason for the failure of computer vision systems. This can also be due to the difficulty of gathering data. A single computer vision system can require a large dataset, including thousands of images and videos which need to be gathered, labeled, and categorized.

For example, in computer vision systems working in the healthcare sector, collecting high-quality medical images is rather difficult due to the sensitive and private nature of the data. This complexity can result in a lack of training for the system.

Therefore, data scientists or annotators must plan the collection of this data before initiating the computer vision project to ensure that the model gets enough training.

MIT media lab has created unorthodox AI architectures of data annotation that can help achieve high-quality training with significantly less amount of data.

3. Install adequate hardware

Another reason for best practice is installing hardware that is capable of supporting the system. This includes computers with strong processing ability, high-definition cameras, sensors, etc. This hardware can be expensive. Therefore, it is important to consider the hardware costs before investing in the technology.

For instance, in a retail surveillance computer vision system, it is important that the cameras are high definition, have an adequate frame per second (FPS) rate, and cover all the space in the retail store.

Walmart started using computer vision-enabled shelf-scanning bots for its retail stores; however, they had to remove them due to the size and shape of the hardware. They later decided that they did not need a separate shelf scanning bot; they could just install the computer vision system as an extension to their autonomous cleaning bots.

An image of the inventory scanning and floor cleaning robots

4. Understand the financial aspects

Computer vision systems are expensive. They require not only expensive hardware but also a time-consuming and expensive development process. For example, a computer vision system in a self-driving taxi can cost from $10,000 to $15,000. This can significantly increase the overall price of the car.

Mammogram scanning computer vision system that assists radiologists in diagnosing breast cancers needs to be trained with hundreds of thousands of mammogram images. These images can also be expensive and difficult to obtain. However, as technology improves, the costs of computer vision systems decrease.

Another example can be a computer vision quality control system. If a manufacturer understands that his/her current production line is making 30% defective products. The manufacturer needs to evaluate if implementing a computer vision-enabled quality control system will help reduce the defects and will the cost savings from the reduced defects cover the investments in the system over time.

You can use the following framework:

An illustration that shows a framework to implement computer vision in your business

5. Understand the development process

Prior to the implementation of the computer vision system, business managers must know all the computer vision techniques which the system uses to analyze the input and process the output. Here are the common techniques:

  • Image classification: This technique is used to classify or categorize objects in the image based on type.
  • Object detection: This technique refers to adding bounding boxes to different objects in the images in order to detect them.
  • Object tracking: This technique tracks the movements of the object and finds it in the next image. Object tracking is used in autonomous car systems to track other vehicles, pedestrians, the road, etc.
  • Image segmentation: The purpose of this technique is to divide objects in the image into pixels to simplify the image.

To learn more about different computer vision techniques and which algorithms are used with those techniques, check out this quick read.

Further reading

If you have any questions, feel free to contact us:

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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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