Why Computer Vision Is Difficult To Implement? (And How To Overcome)

Why Computer Vision Is Difficult

Why Computer Vision Is Difficult To Implement

In this article you will learn more about why computer vision is difficult and complex to implement. Particularly, you will learn about:

  1. The 3 top reasons why computer vision is complex. These are also the main reasons why computer vision projects often fail.
  2. The strategies to overcome the complexity of computer vision implementation at scale.

Every organization is working on Artificial Intelligence driven projects today. However, the charm of AI fades within a short while, when budgets get exhausted, deadlines delayed or ROI metrics not met. The good news? Understanding why computer vision is difficult to implement helps cutting through the complexity. Here’s why:

1.) Cloud Is Not Enough for Most Use Cases

Artificial Intelligence is present in many areas of our lives, providing visible improvements to the way we discover information, communicate or move from point A to point B. AI adoption is rapidly increasing not only in consumer areas such as digital assistants and self-driving vehicles, but across all industries, disrupting whole business models and creating new opportunities to generate new sources of customer value.

Focusing on Visual AI, the number of use-cases for applying AI that performs at human level or better is increasing exponentially, given the fast-paced advances in Machine Learning.

Visual AI encompasses techniques used in the image processing industry to solve a wide range of previously intractable problems by using Computer Vision and Deep Learning.

However, high innovation potential does not come without challenges.

AI inference requires a considerable amount of processing power, especially for real-time data-intensive applications. AI solutions can be deployed in cloud environments (Amazon AWS, Google GCP, Microsoft Azure) in order to take advantage of simplified management and scalable computing assets.

Nevertheless, in most circumstances Cloud is not the adequate environment for deploying Artificial Intelligence.

  • What if your solution needs to run real-time and requires fast response times?
  • How to overcome the challenge if the system is mission critical and running off-grid?
  • What about the huge operating costs of analyzing big volumes of data in the cloud?
  • What about data privacy if sending and storing video material in the cloud?

Therefore, computer vision solutions will need to be deployed on edge endpoints for most use cases. This allows to process the data where it is captured while only the results (light data) are sent back to the cloud for further analysis.

2.) Moving To the Edge Requires Hardware Knowledge

For most use-cases, deploying AI solutions on Edge devices is the only reasonable way to solve the challenge. A fine example is a farming analytics system. The system has to capture and do inference for 30 images per second per camera. For an average setup of 100 cameras, we get a volume of 259.2 million images per day. This is highly inefficient to process in the cloud.

The best option for this use-case is to run inference in real-time at the Edge. Analyse the data where it is being generated! And only communicate key data points to the cloud backend for data aggregation and further analysis. Edge computing is considered to be a current key trend in IT industry.

Considering the rapid growth of AI inference capabilities in Edge hardware platforms (Intel NUC, Intel NCS, Nvidia Jetson, ARM Ethos), transferring the processing requirements from Cloud to Edge becomes a very attractive option for a wide range of businesses.

3.) Complexity Lets Most Organizations Fail at Scaling Visual AI

Even with the promise of great hardware support for Edge deployments, developing a Visual AI solution remains a complex process.

In a traditional approach, several of the following building blocks may be necessary for developing your solution at scale:

  • Collecting input data specific to the problem
  • Expertise with Deep Learning tools like Tensorflow, PyTorch, Keras, Caffe, MXnet for training and evaluating Deep Learning models
  • Selecting the appropriate hardware (e.g. Intel, NVIDIA, ARM) and software platforms (e.g. Linux, Windows, Docker, Kubernetes) and optimizing DL models for the deployment environment
  • Managing deployments to thousands of distributed edge devices
  • Managing updates, data analysis and real-time insights
  • Knowledge about data privacy and security best practices

There is a high level of development risk associated with this approach. Especially when considering development time, required domain experts and difficulties in developing a scalable infrastructure.

Fortunately, there’s a finer path to make your vision a reality, one that allows you to reduce your development costs and time to market by an order of magnitude.

5 Ways To Overcome the Complexity of Computer Vision

viso.ai is an end-to-end cloud platform built to solve AI vision use-cases, with focus on ease-of-use, high performance and scalability. Our platform is industry agnostic. It provides Deep Learning tools to build, deploy and operate deep learning applications in a low-code environment.

viso.ai provides an extensive set of features to reduce the complexity of computer vision at every step of your development cycle. Here’s 5 ways that Viso Suite will use to overcome the challenges:

  1. Visual Programming: Use a visual approach to build complex Visual AI solutions on the fly. The visual programming approach can reduce development time by over 90%. It does not only greatly reduce the effort to write code from scratch, but gives the visibility of how the vision application works.
  2. Device Management: Add and manage thousands of edge devices easily, no matter the device type and architecture (amd64, aarch64, …). Create a device image and flash it to your device to make it appear in your workspace. Check device health metrics, online or deployment statuses without writing a single line of code.
  3. Deployment Management: Deploy AI applications to a large number of edge devices at the click of a button. While you can focus on the algorithm development, deployment, versioning and device management is taken care of for your.
  4. Modular Approach: Benefit from many pre-existing software modules to build your own use case. Viso Suite provides the most common deep learning models for Object Detection, Image Classification, Object Segmentation or Keypoint Detection off-the-shelf. Select the suitable model and create your application with thousands of ready-to-choose logic modules.
  5. Flexibility where needed: Add your own algorithms and code where needed for your custom computer vision solution. Only build code where it does not exist yet and get to market 10x faster and with a very limited risk.

Get started now and be the first to hear exciting news and updates from us about the public release of the viso.ai platform!

What’s Next?

  • Read about viso.ai and how we enable businesses to build Visual AI Applications at scale.
  • Find 5 practical steps to successful implementation (incl. Template) of AI Vision Solution.
  • Read about the Top 10 Edge Endpoints for Computer Vision inference.
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