Performance Evaluation of YOLOv5 and YOLOv8 Object Detection Algorithms on Resource-Constrained Embedded Hardware Platforms for Real-Time Applications

Object detection is a critical task in various real-time applications, including surveillance, autonomous vehicles, and industrial automation, especially within the emerging paradigms of Industry 5.0 and Industrial Internet of Things (IIoT). However, deploying such algorithms on resource-constrained embedded devices poses significant challenges due to their limited computational power and memory resources. This paper presents a comparative evaluation of YOLOv5 and YOLOv8, two state-of-the-art object detection algorithms, on different embedded hardware platforms. Leveraging insights from existing research, we aim to address this work by conducting comprehensive experiments to assess the performance of the proposed algorithm in terms of detection precision and inference speed on each hardware platform. The analysis provided encouraging results and revealed different behaviors, showing the importance of GPUs also for embedded systems.