Benchmarking Deep Learning Models for Object Detection on Edge Computing Devices

Modern applications, such as autonomous vehicles, require deploying deep learning algorithms on resource-constrained edge devices for real-time image and video processing. However, there is limited understanding of the efficiency and performance of various object detection models on these devices. In this paper, we evaluate the performance of several state-of-the-art object detection models, including YOLOv8 (Nano, Small, Medium), EfficientDet Lite (Lite0, Lite1, Lite2), and SSD (SSD MobileNet V1, SSDLite MobileDet), on popular edge devices such as the Raspberry Pi 3, 4, and 5 (with and without TPU accelerators), as well as the Jetson Orin Nano. We collect key performance metrics, including energy consumption, inference time, and Mean Average Precision (mAP). Our findings highlight models with lower mAP such as SSD MobileNet V1 are more energy-efficient and faster in inference, whereas higher mAP models like YOLOv8 Medium generally consume more energy and have slower inference, though with exceptions when accelerators like TPUs are used. Among the edge devices, Jetson Orin Nano stands out as the fastest and most energy-efficient option for request handling, despite having the highest idle energy consumption.