E3-UAV: An Edge-based Energy-Efficient Object Detection System for Unmanned Aerial Vehicles

Motivated by the advances in deep learning techniques, the application of Unmanned Aerial Vehicle (UAV)-based object detection has proliferated across a range of fields, including vehicle counting, fire detection, and city monitoring. While most existing research studies only a subset of the challenges inherent to UAV-based object detection, there are few studies that balance various aspects to design a practical system for energy consumption reduction. In response, we present the E3-UAV, an edge-based energy-efficient object detection system for UAVs. The system is designed to dynamically support various UAV devices, edge devices, and detection algorithms, with the aim of minimizing energy consumption by deciding the most energy-efficient flight parameters (including flight altitude, flight speed, detection algorithm, and sampling rate) required to fulfill the detection requirements of the task. We first present an effective evaluation metric for actual tasks and construct a transparent energy consumption model based on hundreds of actual flight data to formalize the relationship between energy consumption and flight parameters. Then we present a lightweight energy-efficient priority decision algorithm based on a large quantity of actual flight data to assist the system in deciding flight parameters. Finally, we evaluate the performance of the system, and our experimental results demonstrate that it can significantly decrease energy consumption in real-world scenarios. Additionally, we provide four insights that can assist researchers and engineers in their efforts to study UAV-based object detection further.

HIT-UAV: A High-altitude Infrared Thermal Dataset for Unmanned Aerial Vehicles

This paper presents a High-altitude infrared thermal dataset, HIT-UAV, for object detection applications on Unmanned Aerial Vehicles (UAVs). HIT-UAV contains 2898 infrared thermal images extracted from 43470 frames. These images are collected by UAV from schools, parking lots, roads, playgrounds, etc. HIT-UAV provides different flight data for each place, including flight altitude (from 60 to 130 meters), camera perspective (from 30 to 90 degrees), date, and daylight intensity. For each image, the HIT-UAV manual annotates object instances with two types of the bounding box (oriented and standard) to address the challenge that object instances have a significant overlap in aerial images. To the best of our knowledge, HIT-UAV is the first publicly available high-altitude infrared thermal UAV dataset for persons and vehicles detection. Moreover, we trained and evaluated the benchmark detection algorithms (YOLOv4 and YOLOv4-tiny) on HIT-UAV. Compared to the visual light dataset, the detection algorithms have excellent performance on HIT-UAV because the infrared thermal images do not contain a significant quantity of irrelevant information with detection objects. This indicates that infrared thermal datasets can significantly promote the development of object detection applications. We hope HIT-UAV contributes to UAV applications such as traffic surveillance and city monitoring at night. The dataset is available at https://github.com/suojiashun/HIT-UAV-Infrared-Thermal-Dataset.