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

Mittal, Payal; Singh, Raman; Sharma, Akashdeep

doi:10.1016/j.imavis.2020.104046

Cited by 159 publications

(47 citation statements)

References 30 publications

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“…The system architecture and POMDP motion planner presented in this paper improve the autonomy capabilities of small UAVs under environment and object detection uncertainty. Under the presence of uncertainty from potential incorrect readings of vision-based detectors, such as CNNs, the standard objective of many existing approaches is to improve the performance metrics of their customised CNN-based object detectors for real-world UAV operations [40]. In contrast, this paper proposes an alternative approach to this uncertainty issue by augmenting the cognitive power onboard UAVs.…”

Section: Discussionmentioning

confidence: 99%

Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty

et al. 2021

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Recent advances in autonomy of unmanned aerial vehicles (UAVs) have increased their use in remote sensing applications, such as precision agriculture, biosecurity, disaster monitoring, and surveillance. However, onboard UAV cognition capabilities for understanding and interacting in environments with imprecise or partial observations, for objects of interest within complex scenes, are limited, and have not yet been fully investigated. This limitation of onboard decision-making under uncertainty has delegated the motion planning strategy in complex environments to human pilots, which rely on communication subsystems and real-time telemetry from ground control stations. This paper presents a UAV-based autonomous motion planning and object finding system under uncertainty and partial observability in outdoor environments. The proposed system architecture follows a modular design, which allocates most of the computationally intensive tasks to a companion computer onboard the UAV to achieve high-fidelity results in simulated environments. We demonstrate the system with a search and rescue (SAR) case study, where a lost person (victim) in bushland needs to be found using a sub-2 kg quadrotor UAV. The navigation problem is mathematically formulated as a partially observable Markov decision process (POMDP). A motion strategy (or policy) is obtained once a POMDP is solved mid-flight and in real time using augmented belief trees (ABT) and the TAPIR toolkit. The system’s performance was assessed using three flight modes: (1) mission mode, which follows a survey plan and used here as the baseline motion planner; (2) offboard mode, which runs the POMDP-based planner across the flying area; and (3) hybrid mode, which combines mission and offboard modes for improved coverage in outdoor scenarios. Results suggest the increased cognitive power added by the proposed motion planner and flight modes allow UAVs to collect more accurate victim coordinates compared to the baseline planner. Adding the proposed system to UAVs results in improved robustness against potential false positive readings of detected objects caused by data noise, inaccurate detections, and elevated complexity to navigate in time-critical applications, such as SAR.

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Section: Discussionmentioning

confidence: 99%

Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty

et al. 2021

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“…In a recent survey published by Mittal et al, they discussed the algorithms namely Faster RCNN, Cascade RCNN, R-FCN, YOLO and its variants, SSD, RetinaNet and Cor-nerNet, Objects as Point under advanced phases in detectors based on deep learning. This paper provides a comprehensive summary of low-altitude datasets and the algorithms used for the respective work [12]. Our comparison work was done using coco metrics similar to the comparison that has been done in this paper.…”

Section: Literature Surveymentioning

confidence: 99%

Comparative analysis of deep learning image detection algorithms

et al. 2021

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A computer views all kinds of visual media as an array of numerical values. As a consequence of this approach, they require image processing algorithms to inspect contents of images. This project compares 3 major image processing algorithms: Single Shot Detection (SSD), Faster Region based Convolutional Neural Networks (Faster R-CNN), and You Only Look Once (YOLO) to find the fastest and most efficient of three. In this comparative analysis, using the Microsoft COCO (Common Object in Context) dataset, the performance of these three algorithms is evaluated and their strengths and limitations are analysed based on parameters such as accuracy, precision and F1 score. From the results of the analysis, it can be concluded that the suitability of any of the algorithms over the other two is dictated to a great extent by the use cases they are applied in. In an identical testing environment, YOLO-v3 outperforms SSD and Faster R-CNN, making it the best of the three algorithms.

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“…Deep learning object detection with UAVs is an attractive application, and, as such, it attracts the interest of many researchers. A large portion of the presented solutions relies on onboard processing [42]. However, the applications focus mostly on the detection of objects such as persons and vehicles [43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Autonomous, Onboard Vision-Based Trash and Litter Detection in Low Altitude Aerial Images Collected by an Unmanned Aerial Vehicle

et al. 2021

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Public littering and discarded trash are, despite the effort being put to limit it, still a serious ecological, aesthetic, and social problem. The problematic waste is usually localised and picked up by designated personnel, which is a tiresome, time-consuming task. This paper proposes a low-cost solution enabling the localisation of trash and litter objects in low altitude imagery collected by an unmanned aerial vehicle (UAV) during an autonomous patrol mission. The objects of interest are detected in the acquired images and put on the global map using a set of onboard sensors commonly found in typical UAV autopilots. The core object detection algorithm is based on deep, convolutional neural networks. Since the task is domain-specific, a dedicated dataset of images containing objects of interest was collected and annotated. The dataset is made publicly available, and its description is contained in the paper. The dataset was used to test a range of embedded devices enabling the deployment of deep neural networks for inference onboard the UAV. The results of measurements in terms of detection accuracy and processing speed are enclosed, and recommendations for the neural network model and hardware platform are given based on the obtained values. The complete system can be put together using inexpensive, off-the-shelf components, and perform autonomous localisation of discarded trash, relieving human personnel of this burdensome task, and enabling automated pickup planning.

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

Cited by 159 publications

References 30 publications

Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty

Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty

Comparative analysis of deep learning image detection algorithms

Autonomous, Onboard Vision-Based Trash and Litter Detection in Low Altitude Aerial Images Collected by an Unmanned Aerial Vehicle

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