Recently, Shenzhen LeaKin Technology Co., Ltd. successfully hosted an internal technical training session and customer forum themed “Edge AI Vision Deployment: Practical Implementation and Challenges of the YOLO Model on the STM32 Platform.” The event focused on current pain points and cutting-edge trends in the embedded AI field, bringing together the company’s core technical team and numerous key customer representatives for in-depth discussions and knowledge sharing on deploying object-detection algorithms on microcontrollers (MCUs).

With the rapid advancement of the Internet of Things and edge computing, embedding AI algorithms directly into edge devices has become an industry-wide consensus. However, in real-world deployments, a central concern for many customers is whether object-detection models like YOLO can run on STM32 microcontrollers. Addressing this key technical issue, technical experts from LeaKin Technology delivered a comprehensive and insightful analysis at the event.

The conference clearly stated that YOLO (You Only Look Once), as a mainstream object detection model, must not only perform image classification but also accurately output the target’s location coordinates, category, and confidence score—requirements that place extremely high demands on an MCU’s computational power, memory, and real-time frame rate. Consequently, whether YOLO can be deployed on an STM32 cannot be addressed in general terms without considering the specific chip model.

During the technical sharing session, LeaKin Technology’s engineering team provided detailed deployment assessments and component‑selection recommendations for the various STM32 series.

• For entry-level MCUs such as the STM32F103, direct deployment of YOLO is impractical due to limitations in RAM, flash memory, and clock speed; these chips are better suited for running simple sensor‑based classification tasks or small regression models.
• For the STM32F4 and certain F7 series: deploying a full‑size YOLO model still faces significant computational and memory constraints. We recommend cautiously experimenting with very lightweight detection models, or shifting to lighter‑weight tasks such as audio keyword spotting.
• For the STM32H7 series: Although performance has improved significantly, in the absence of a dedicated NPU, running YOLO still requires substantial model pruning and quantization, as well as a reduction in input resolution, making it suitable for lightweight vision‑based detection scenarios with low frame rates and a limited number of classes.
• For the STM32N6 series: As a next-generation chip designed for edge AI and visual inference, its built-in Neural‑ART NPU provides an ideal runtime environment for lightweight models such as YOLOv8n and Tiny YOLO, delivering exceptional real-world deployment value.

Technical experts emphasize that successfully deploying YOLO on STM32 is not merely a matter of model conversion—it is a full‑scale systems engineering effort. It requires rigorous end‑to‑end processing: selecting a lightweight model, controlling the input resolution, performing INT8 quantization, verifying operator support, generating deployment code using toolchains such as STM32Cube.AI, and integrating camera interfaces, image preprocessing, NMS post‑processing, and more. Each step is critical. The real challenge lies in ensuring that the entire pipeline runs reliably on the target hardware.

During the subsequent customer‑engagement session, attendees engaged in in‑depth discussions with the LeaKin Technology team, sharing the specific pain points they face in their respective projects. Customers highly commended LeaKin Technology for its deep technical expertise and pragmatic approach in the edge AI space. All participants agreed that this training not only clarified common technical misconceptions but also provided a clear roadmap for future product selection and solution implementation.

Shenzhen LeaKin Technology Co., Ltd. is committed to delivering cutting-edge, reliable embedded AI solutions to its customers. Looking ahead, the company will continue to deepen its expertise in edge computing and machine vision, leveraging ongoing technological innovation and professional technical services to help clients transcend computational limitations and accelerate the intelligent deployment of AI across a wide range of industries.