Containerized Computer Vision Applications on Arm-Powered Edge Devices


The rise of IoT devices has led to an increased use of computer vision applications. However, the traditional IoT-Cloud model struggles to process image data efficiently due to bandwidth and latency issues. To overcome these obstacles, edge nodes have been introduced between IoT devices and the cloud. However, the deployment and securing of computer vision applications on these nodes remains a challenge. Container technology offers a promising solution, but its performance in specific domains has not been fully investigated.

In response to this challenge, this doctoral dissertation explores the effectiveness of using lightweight container technology to deploy CPU-based computer vision applications on edge devices through three primary approaches. First, it assesses various container technologies and images in computer vision applications and scrutinizes their performance across various CPUs and GPUs on ARM-based edge devices. Second, it constructs an optimized OpenCV container to serve as the foundational image for containerized computer vision applications. Lastly, the performance of the optimized OpenCV container is analyzed to optimize the performance of the cluster for computer vision applications while accommodating numerous IoT sensors. Through various experiments, this doctoral dissertation extensively analyzes the performance of various containerized AI vision applications at the edge and demonstrates how to boost their efficiency by carefully integrating container technologies into IoT and ARM-based edge computing environments.

In conclusion, this doctoral dissertation improves the understanding of integrating AI vision into IoT and edge computing, paving the way for deploying AI vision capabilities in real-time, edge-driven smart environments.



Computer Vision, Container Technology, Docker, Edge Computing, Internet of Things, OpenCV



Computer Science