What common problems do companies face with computer vision?
Companies starting out in using computer vision technology often face multiple issues that can delay efficiency in their system. The most common issues include the following:
- Messy or Incomplete Datasets: Artificial Intelligence (AI) models require a large amount of high-quality visual data for training. If images and videos are not properly labeled or have missing details, the system cannot create accurate AI models. Without proper annotation and labeling systems, the overall performance of AI-powered vision systems suffers.
- High Costs and Resource Requirements: To develop deep learning models, a high-performance computing power system must be in place. However, most companies cannot afford expensive GPUs and cloud-based computing solutions.
- Integration with Legacy Systems: Many companies in different sectors, such as healthcare, fintech, e-commerce, real estate, and automotive, rely on older systems that are not suitable for AI-powered automation.
- Scalability Concerns: With the company’s growth, more big data are generated. This requires their AI platform to process them efficiently. However, without a scalable computer vision platform, companies may experience performance delays.
- Privacy, Security, and Compliance Issues: Companies working with social media, fintech, and real estate are often subjected to strict regulations on visual data privacy. Using AI-powered image recognition technology in those industries could lead to data privacy concerns.