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LightDenseYOLO: A Fast and Accurate Marker Tracker for Autonomous UAV Landing by Visible Light Camera Sensor on Drone

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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SSD: Single Shot MultiBox Detector

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Monocular vision-based real-time target recognition and tracking for autonomously landing an UAV in a cluttered shipboard environment

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Airborne Vision-Based Navigation Method for UAV Accuracy Landing Using Infrared Lamps

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Vision Based Autonomous Landing of Multirotor UAV on Moving Platform

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The Pascal Visual Object Classes Challenge: A Retrospective

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Use of land’s cooperative object to estimate UAV’s pose for autonomous landing

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Research on computer vision-based for UAV autonomous landing on a ship

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A deep learning approach to drone monitoring

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ImageNet: A large-scale hierarchical image database

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Deep Residual Learning for Image Recognition

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You Only Look Once: Unified, Real-Time Object Detection

inferredcites →https://doi.org/10.1109/cvpr.2016.97openalex-snapshot

Object-Proposal Evaluation Protocol is ‘Gameable’

inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-snapshot

Densely Connected Convolutional Networks

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Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors

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YOLO9000: Better, Faster, Stronger

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Towards autonomous landing on a moving vessel through fiducial markers

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Convolutional Neural Network-Based Deep Urban Signatures with Application to Drone Localization

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Forward-Backward Error: Automatic Detection of Tracking Failures

inferredcites →https://doi.org/10.1109/icra.2015.7138988openalex-snapshot

Continuous on-board monocular-vision-based elevation mapping applied to autonomous landing of micro aerial vehicles

inferredcites →https://doi.org/10.1109/irc.2017.77openalex-snapshot

Real-Time, Cloud-Based Object Detection for Unmanned Aerial Vehicles

inferredcites →https://doi.org/10.1109/iros.2016.7759617openalex-snapshot

AprilTag 2: Efficient and robust fiducial detection

inferredcites →https://doi.org/10.1109/iros.2017.8206247openalex-snapshot

Learning to fly by crashing

inferredcites →https://doi.org/10.1109/iros.2017.8206285openalex-snapshot

Toward low-flying autonomous MAV trail navigation using deep neural networks for environmental awareness

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Drone Classification Using Convolutional Neural Networks With Merged Doppler Images

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A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots

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Vision-based autonomous quadrotor landing on a moving platform

inferredcites →https://doi.org/10.1109/tpami.2011.239openalex-snapshot

Tracking-Learning-Detection

inferredcites →https://doi.org/10.1109/tpami.2014.2345390openalex-snapshot

High-Speed Tracking with Kernelized Correlation Filters

inferredcites →https://doi.org/10.1109/tpami.2015.2465908openalex-snapshot

What Makes for Effective Detection Proposals?

inferredcites →https://doi.org/10.1109/tpami.2016.2577031openalex-snapshot

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

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A New Approach to Linear Filtering and Prediction Problems

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Autonomous landing of a helicopter UAV with a ground-based multisensory fusion system

inferredcites →https://doi.org/10.1145/2647868.2654889openalex-snapshot

Caffe

inferredcites →https://doi.org/10.1145/3065386openalex-snapshot

ImageNet classification with deep convolutional neural networks

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Deep Learning Quadcopter Control via Risk-Aware Active Learning

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Human crowd detection for drone flight safety using convolutional neural networks

inferredcites →https://doi.org/10.3390/jimaging3020021openalex-snapshot

Object Recognition in Aerial Images Using Convolutional Neural Networks

inferredcites →https://doi.org/10.3390/s16091393openalex-snapshot

A Ground-Based Near Infrared Camera Array System for UAV Auto-Landing in GPS-Denied Environment

inferredcites →https://doi.org/10.3390/s17091987openalex-snapshot

Remote Marker-Based Tracking for UAV Landing Using Visible-Light Camera Sensor

inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot

Very Deep Convolutional Networks for Large-Scale Image Recognition

inferredcites →https://doi.org/10.5244/c.28.24openalex-snapshot

How good are detection proposals, really?

inferredcites →https://doi.org/10.5772/62027openalex-snapshot

Ground Stereo Vision-Based Navigation for Autonomous Take-off and Landing of UAVs: A Chan-Vese Model Approach

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Autopilot Drone in Construction:A Proof of Concept for Handling Lightweight Instruments and Materials

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Improved You Only Look Once Model for UAVs/Ships Relative Attitude Detection

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Predictive precision of enhanced drone landings

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Mountain Rescue Operations Facilitated with Drone Usage

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