A review on object detection in unmanned aerial vehicle surveillance

• Published in: International journal of cognitive computing in engineering Vol. 2; pp. 215 - 228
• Main Authors: Ramachandran, Anitha, Sangaiah, Arun Kumar
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2021; KeAi Communications Co., Ltd
• Subjects: Deep learning; Drone; Object detection; Precision agriculture; Security; UAV; Deep learning; Precision agriculture; UAV; Drone; Security; Object detection
• ISSN: 2666-3074; 2666-3074
• DOI: 10.1016/j.ijcce.2021.11.005

•In this paper, a detailed literature review has been conducted focusing on object detection and tracking using UAVs concerning different applications.•Contribution: Object detection methods applied in UAV images are classified and elaborated. UAV datasets specific to object detection tasks are listed.•Existing research works in different applications are summarized.•Finally, a secure onboard processing system on a robust object detection framework in precision agriculture is proposed to mitigate identified research gaps. Computer vision in drones has gained a lot of attention from artificial intelligence researchers. Providing intelligence to drones will resolve many real-time problems. Computer vision tasks such as object detection, object tracking, and object counting are significant tasks for monitoring specified environments. However, factors such as altitude, camera angle, occlusion, and motion blur make it a more challenging task. In this paper, a detailed literature review has been conducted focusing on object detection and tracking using UAVs concerning different applications. This study summarizes the findings of existing research papers and identifies the research gaps. Object detection methods applied in UAV images are classified and elaborated. UAV datasets specific to object detection tasks are listed. Existing research works in different applications are summarized. Finally, a secure onboard processing system on a robust object detection framework in precision agriculture is proposed to mitigate identified research gaps.