ABSTRACT

Recent advancements in the field of Artificial Intelligence (AI) have provided an opportunity to create autonomous devices, robots, and machines characterised particularly with the ability to make decisions and perform tasks without human mediation. One of these devices, Unmanned Aerial Vehicles (UAVs) or drones are widely used to perform tasks like surveillance, search and rescue, object detection and target tracking, and many more. Efficient real-time object detection in aerial videos is an urgent need, especially with the increasing use of UAV in various fields. The sensitivity in performing said tasks demands that drones must be efficient and reliable. This paper presents our research progress in the development of applications for the identification and detection of person using the convolutional neural networks (CNN) YOLO-v2 based on the camera of drone. The position and state of the person are determined with deep-learning-based computer vision. The person detection results show that YOLO-v2 detects and classifies object with a high level of accuracy. For real-time tracking, the tracking algorithm responds faster than conventionally used approaches, efficiently tracking the detected person without losing it from sight.

摘要

人工智能 (AI) 领域的最新进展为创建自主设备、机器人和机器提供了机会，这些设备的特点尤其是能够在没有人工干预的情况下做出决策和执行任务。其中一种设备是无人驾驶飞行器 (UAV) 或无人机，广泛用于执行监视、搜索和救援、物体检测和目标跟踪等任务。航拍视频中高效的实时目标检测是迫切需要的，尤其是随着无人机在各个领域的使用越来越多。执行上述任务的敏感性要求无人机必须高效可靠。本文介绍了我们在使用基于无人机摄像头的卷积神经网络 (CNN) YOLO-v2 开发人员识别和检测应用方面的研究进展。人的位置和状态由基于深度学习的计算机视觉确定。人检测结果表明，YOLO-v2 以较高的准确度检测和分类对象。对于实时跟踪，跟踪算法的响应速度比传统使用的方法更快，有效地跟踪检测到的人而不会失去视线。