Affiliation:
1. Department of Computer Engineering, College of Computers and Information Technology, Taif University, Saudi Arabia
2. Department of electrical engineering, Alzaiem Alazhari University, Sudan
Abstract
This chapter provides an overview of the diverse applications of ML in UAVs for object and people detection and tracking. It begins by examining the current landscape of ML-driven UAV technologies and their potential. The related work section discusses the advancements in object and people detection and tracking. The subsequent sections delve into the technical aspects, focusing on the next generation of UAV convolutional neural network (CNN) backbones, including the contextual multi-scale region-based CNN (CMSRCNN), single shot multibox detector (SSD), and you only look once (YOLO), highlighting their significance in enhancing detection capabilities. Furthermore, it explores practical applications of ML in UAVs, encompassing object and people detection and tracking, path planning, navigation, and image and video analysis. Challenges and complexities in vision-based UAV navigation are addressed. Additionally, it showcases the potential for UAV networks to locate objects in real time.