The rise of practical, cost-effective AI has reset expectations for Digital Security and Surveillance (DSS). Security professionals now expect features like crowd counting, license plate recognition, and suspicious object detection to be standard in every DSS system.
This creates a pressing challenge for OEMs and system designers: how to deploy AI models quickly enough to keep pace with escalating customer demands, and to make these models execute in the field and therefore be independent of cloud-based data centers. Developing custom models in-house is often too slow and costly, making off-the-shelf AI models an attractive alternative for rapid deployment.
Among the leading providers are Ultralytics YOLO, Hugging Face, and Mistral. Each offers computer vision solutions designed for edge deployment—optimizing latency, cost, and privacy by keeping processing local. Let’s take a look at factors to consider when choosing a model, including performance, ease of deployment, and licensing requirements.
Ultralytics YOLO: The Computer Vision Specialist
Of the three options, YOLO is the only one purpose-built for computer vision (CV) — and as a result, it often achieves the best performance. But speed and accuracy are not its only advantages: flexibility is another big plus. The YOLO ecosystem includes everything from the full-featured YOLOv8 to lightweight options like YOLOv10-Nano, which delivers over 90% of the accuracy of its bigger sibling in a tiny 5 MB package—perfect for edge deployments.
YOLO models can be deployed on a wide variety of AI hardware platforms, including GPUs and specialized AI accelerators. Developers typically work within the Python ecosystem, but conversion tools are available to support other programming environments. A key strength of YOLO is its mature documentation and community support. However, a commercial license is required for production use, which may be an issue for cost-constrained projects.
Hugging Face: The Open-Source Powerhouse
Hugging Face offers a broad, open-source alternative. While best known for natural language processing, Hugging Face also provides a vast library of pre-trained computer vision models. These include not only standard object detection models but also advanced multimodal models that combine vision and language capabilities — for instance, answering questions about an image or performing optical character recognition (OCR).
The Hugging Face Transformers library simplifies model fine-tuning and deployment, offering a flexible framework for developers. While many Hugging Face models are available under permissive licenses like Apache 2.0, some require commercial licenses. It’s therefore essential to review the terms of these licenses carefully.
Mistral: Opening DSS AI to All Developers
Mistral, another open-source provider, focuses on large language models (LLMs) with expanding support for vision tasks. Mistral’s platform stands out for its support of more than 80 programming languages, offering developers flexibility beyond Python.
Although Mistral is not specialized in high-speed object detection like YOLO, its models excel at generating detailed text-based insights from images, making it well-suited for applications that require contextual analysis.
Mistral’s models can also be used in conjunction with Hugging Face’s tools, allowing hybrid approaches. Proprietary models are clearly marked, making it easier for developers to navigate licensing decisions.
Which AI Model is Right for Your DSS Project?
Clearly, each platform offers distinct advantages that align with different development priorities. We can summarize these as follows:
- Choose YOLO if your primary need is high-speed, accurate object detection and tracking. Its purpose-built architecture delivers industry-leading results. Just remember to account for commercial licensing in your project budget.
- Choose Hugging Face for flexibility and deep customization within the Python ecosystem. If you need to fine-tune models or want to combine vision capabilities with language understanding, Hugging Face’s vast model library and powerful customization tools provide the most versatile foundation. Pay careful attention to licensing terms, as they vary by model.
- Choose Mistral if you need broad programming language support or aim to combine vision with detailed analysis capabilities. Mistral’s support for over 80 programming languages makes it accessible to development teams without Python expertise, while its strength in language models enables more sophisticated interpretation of surveillance footage.
Hardware Options for Rapid Deployment
SECO offers multiple hardware platforms optimized for running these AI models at the edge and has also successfully implemented YOLO models on the Axelera AI Metis platform, which delivers 15 TOPS per watt of AI processing power. This AIPU has been integrated with SECO's Palladio 500 RPL modular embedded PC to create a complete computer vision AI pipeline from edge to cloud, offering an optimized solution for demanding surveillance applications.
The Next Market Shift in Surveillance
The widespread adoption of AI is transforming the video surveillance market, much like the transition from analog to digital systems at the start of the millennium. As before, the OEMs and system designers who move fastest to adopt new technologies will gain a significant competitive edge. Today, that often means relying on ready-to-use AI libraries and proven off-the-shelf hardware to accelerate development and deployment.
Ready to transform your DSS systems with AI? Contact SECO to discover how we can accelerate your journey.