Navigating Real-World Challenges in Computer Vision: A Practical Guide Based on Personal Experience| Part I
Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering,it seeks to understand and automate tasks that the human visual system can do.
In the following article, I discuss real computer vision issues and How to use my experience to handle this kind of situation.
I believe you can connect to this article, and if you’re just starting off, it will be beneficial.
However, computer vision has many vital values because the main reason we want to develop computer vision is to create artificial human eyesight.
Machine vision vs Human vision:
Machine vision and human vision refer to the ability of machines and humans, espectively, to perceive and interpret visual information.
Machine vision is powerful for specific tasks and can excel in speed and precision, it often lacks the versatility, adaptability, and nuanced understanding that human vision naturally possesses. Integrating the strengths of both human and machine vision can lead to more robust and effective visual perception systems.
Importance of Computer vision:
Computer Vision’s importance Real-time video stream analysis, variable conversion, and logic process application are all capabilities that computers can use to tackle challenging visual problems. Based on artificial intelligence, a computer can identify shapes and count items more quickly and accurately than a human can when it comes to object detection. A computer can also do such a work swiftly and independently as many times as necessary. Numerous real-world computer vision applications in a variety of industries rely on this. A computer can often perform a visual task more consistently and precisely than a human can in many circumstances.
The benefits of automating any manual operation are essentially the same. Therefore, it should come as no surprise that a large number of offline firms may use computers to improve the quality of their goods or services and/or reduce operating expenses. However, the practical use of computer vision and visual AI in general is fraught with significant risks.
Major real time computer vision problem:
These days, the most common real-time computer vision applications are computer problems with vision, task classification, object recognition, segmentation, and keypoint detection. Finally, we will move on to 3D computer vision, which is a different problem than the previous 4.So, how does everyone think it developed?
First, we will short discuss about major problem of computer vision?
I think you have a clear understanding of all the key terms related to computer vision challenges after looking at the above images.
Above all, I go over a lot of the details from my prior article, but we’ll be concentrating on problems that arise in the real world and how to handle them. First, I encourage you to share your experiences with me if there is anything improper with an article.
Major Problems and solution:
Real-time computer vision presents several challenges, and addressing these problems requires a combination of algorithmic, hardware, and system-level optimizations. Here are some common problems and potential solutions:
- Processing Speed:
- Problem: Achieving low-latency processing for real-time applications can be challenging.
- Solution: Optimize algorithms for efficiency, leverage parallel processing with GPUs or TPUs, and consider using dedicated vision processing units (VPUs) for specific tasks.
2. Hardware Limitations:
- Problem: The efficiency of real-time computer vision is dependent on hardware capabilities.
- Solution: Choose hardware that is well-suited for real-time tasks, such as GPUs or specialized accelerators. Optimize code for parallel processing and consider deploying on edge devices.
3. Accuracy vs. Speed Trade-off:
- Problem: Balancing accuracy and speed is a common challenge in real-time applications.
- Solution: Use model quantization, pruning, or trade accuracy for speed by choosing simpler model architectures. Explore real-time-specific architectures like MobileNet or SqueezeNet.
4. Data Variability:
- Problem: Real-world data can be highly variable, requiring models to generalize well.
- Solution: Augment training data to simulate different conditions, use transfer learning to leverage pre-trained models, and ensure a diverse dataset to cover a wide range of scenarios.
5. Robustness to Noise and Occlusions:
- Problem: Real-time systems need to handle noisy or occluded input data.
- Solution: Apply robust pre-processing techniques, use feature extraction methods that are less sensitive to noise, and incorporate techniques like data denoising or outlier rejection.
6. Energy Efficiency:
- Problem: Real-time applications running on battery-powered devices need to be energy-efficient.
- Solution: Optimize algorithms for energy efficiency, use low-power hardware, implement efficient coding practices, and explore techniques like model pruning to reduce computational load.
7. Adaptability to Dynamic Environments:
- Problem: Real-time systems should adapt to changes in the environment.
- Solution: Implement dynamic calibration, update models periodically, and use techniques like online learning to adapt to evolving conditions.
8. Real-world Testing and Evaluation:
- Problem: Evaluating performance in real-world conditions can be challenging.
- Solution: Conduct extensive testing in diverse environments, use simulation tools to recreate real-world scenarios, and continuously monitor and update models based on real-world performance.
7. Privacy and Ethical Concerns:
- Problem: Real-time computer vision systems may raise privacy concerns.
- Solution: Implement privacy-preserving techniques, such as on-device processing, anonymization of data, and adherence to ethical guidelines and regulations.
8. Scalability:
- Problem: Ensuring scalability as complexity increases is crucial.
- Solution: Design systems with scalability in mind, consider distributed computing for resource-intensive tasks, and leverage cloud services when necessary.
Addressing these problems often involves a multidisciplinary approach, with collaboration between computer vision experts, hardware engineers, and system architects. Continuous monitoring, updates, and improvements based on real-world feedback are essential for successful real-time computer vision applications.
AI, in my opinion, is at the core of industries like manufacturing, gaming, the web, etc. However, there are two key points to remember: testing is crucial for AI. AI engineers now test and deliver projects or products as well. However, management of the testing crew is crucial for an AI product. And also below mention point is precious.
- Domain-specific expertise: Understanding the specific application domain and its challenges is crucial for tailoring solutions and selecting appropriate techniques.
- Continuous evaluation and improvement: Real-world systems require ongoing monitoring, performance evaluation, and adaptation to changing conditions and evolving needs.
This above all things are Major problem and solution.
The availability of CPU resources affects computer vision activities’ accuracy and delay. As a result, models with higher accuracy (such Mask R-CNN) typically require a lot more resources. This is very important, especially for large-scale AI vision systems. When lower-quality gear produces comparable outcomes, cost reductions quickly reach the millions. Unfortunately, because they rely on a very “heavy” (computationally intensive) model that necessitates expensive hardware like GPUs, many visual AI solutions are not feasible in production. As a result, the setup expenditures would not be offset by the economic gain realised. But I’m happy to tell you! Computers are cheaper and more powerful every year as computing costs drop dramatically.
Thus, “heavy” models can be applied to a wider range of scenarios, and significant performance benefits can be achieved by upgrading the hardware to leverage contemporary AI accelerators. There are several options available to you if waiting or exchanging the AI hardware is not an option. Reducing the Frames per Second (FPS) considerably can fix a lot of visual difficulties. Processing just one frame per second, or fewer, can result in substantially higher precision, for example, if you are counting static objects. Unexpected as it may seem, there may be a significant difference in the application’s perceived quality.
Simply says,
- High Computational cost
- Object visibility issues
- Processing Speed issues (Low FPS, system configure)
- Low quality Data
- Lighting Issues.
Computer vision is everywhere, because we want to develop the artifical vision and brain.
Furthermore, environmental factors and lighting pose significant challenges to computer vision. I’ll talk about part two articles.
I appreciate you coming, people. Please leave a comment if this article contains any questions or errors. or let’s connect via kaggle Conversation or LinkedIn.
