Deep learning-based object detection in low-altitude UAV datasets: A survey

Abstract

Deep learning-based object detection solutions emerged from computer vision has captivated full attention in recent years. The growing UAV market trends and interest in potential applications such as surveillance, visual navigation, object detection, and sensors-based obstacle avoidance planning have been holding good promises in the area of deep learning. Object detection algorithms implemented in deep learning framework have rapidly became a method for processing of moving images captured from drones. The primary objective of the paper is to provide a comprehensive review of the state of the art deep learning based object detection algorithms and analyze recent contributions of these algorithms to low altitude UAV datasets. The core focus of the studies is low-altitude UAV datasets because relatively less contribution was seen in the literature when compared with standard or remote-sensing based datasets. The paper discusses the following algorithms: Faster RCNN, Cascade RCNN, R-FCN etc. into two-stage, YOLO and its variants, SSD, RetinaNet into one-stage and CornerNet, Objects as Point etc. under advanced stages in deep learning based detectors. Further, one-two and advanced stages of detectors are studied in detail focusing on low-altitude UAV datasets. The paper provides a broad summary of low altitude datasets along with their respective literature in detection algorithms for the potential use of researchers. Various research gaps and challenges for object detection and classification in UAV datasets that need to deal with for improving the performance are also listed.