The convergence of AI and edge computing is revolutionizing industries by facilitating real-time decision-making and intelligent operations. One area with tremendous potential is edge computer vision, which can be used in edge locations to improve productivity, reduce safety risks, and avoid the costs of losses and defects. These real world use cases depend on the ability to effectively and securely process video data at the edge. In fact, according to Gartner, by 2030 80% of enterprise video will be automatically processed at the edge, up from only 20% in 2024.
The improvements in computer vision models and lower costs of camera hardware are making the return on investment in computer vision at the edge more and more attractive. However, there are still challenges to overcome, both with the specifics of computer vision and the realities of any computing at the edge.
In this series of blogs, ZEDEDA will explore foundational concepts and requirements for companies to successfully harness the benefits of computer vision at the edge.
Understanding the Edge and Its Challenges
Edge computing involves processing camera video data closer to its source, reducing latency and enabling real-time decision-making. The video data generated at the edge is vast and exceeds the combined data generated on the internet. Processing this data requires edge devices capable of handling the influx. Edge devices, essentially small, hardened computers, are geographically distributed in various locations, including tops of poles, roadside cabinets, drilling sites, and vans.
However, edge devices often have limited computing resources, power, and connectivity. This makes it challenging to manage any software running on edge systems securely and efficiently, let alone rapidly iterating computer vision models. Factors such as data variability, model degradation, and the need for models to function offline due to limited connectivity need to be addressed for successful AI deployment at the edge.
The Role of Data Handling and Contextualization
Data handling and contextualization are critical for building robust AI models at the edge. An edge AI development platform, like Edge Impulse, simplifies this process by providing tools for labeling, synthetic data generation, and seamless integration with cloud storage. The platform should offer various options for adding data, including cloud bucket connectors and pre-processing features. It should also have AI labeling features that can automate a significant portion of the labeling process, saving time and effort. As demonstrated in this recent webinar on computer vision at the edge, Edge Impulse also supports model optimization techniques like quantization to improve performance on resource-constrained devices.
Managing Diverse AI Applications on Heterogeneous Devices
Deploying software, let alone AI models, on a variety of edge devices can be complex. An edge computing platform addresses this challenge by providing a hardware-agnostic foundation that supports multiple frameworks and workload types across different hardware configurations. An edge computing platform should also facilitate the use of MLops tools for efficient model management and upgrades. ZEDEDA offers an edge computing platform that ensures that models developed on Edge Impulse can run seamlessly on any hardware, thanks to its hardware-agnostic approach. This approach allows for efficient model deployment and management across diverse edge devices.
Learn more about how ZEDEDA and Edge Impulse are partnering to streamline the development, deployment, and management of computer vision AI models at the edge.
Security Concerns for Edge Computer Vision
Maintaining data integrity and ensuring secure edge device integration is paramount. Computer vision models are processing potentially sensitive image data, and the devices they are running on are typically proximate to mission-critical systems. Tampering with a camera feed, a computer vision model, or supporting systems, can undermine the benefits of computer vision at the edge and expose an organization to bad actors.
In addition to standard cybersecurity practices, computer vision at the edge requires some additional considerations, given the remote nature of the edge devices they are running on. Edge devices need to be authenticated securely and quarantined remotely if any unauthorized changes are detected. Communication between edge devices and centralized systems, which can be intermittent, needs to be secured. Encryption is also required to protect sensitive data and prevent unauthorized access. Every layer of interaction with hardware and software needs to be secured, as well as remote management abilities and extensibility for additional security layers as needed.
By addressing these key considerations, businesses can successfully implement computer vision at the edge and unlock its full potential.
Learn more about how implementing edge computer vision can help drive improved safety, productivity and efficiency.
