Journal of Computer and Communications

Journal of Computer and Communications

ISSN Print: 2327-5219
ISSN Online: 2327-5227
www.scirp.org/journal/jcc
E-mail: jcc@scirp.org
"A Vehicle Detection Method for Aerial Image Based on YOLO"
written by Junyan Lu, Chi Ma, Li Li, Xiaoyan Xing, Yong Zhang, Zhigang Wang, Jiuwei Xu,
published by Journal of Computer and Communications, Vol.6 No.11, 2018
has been cited by the following article(s):
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[2] Training YOLO v4 on DOTA dataset
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[3] The Detection of a Single Wet Rice Field Bund on Unmanned Aerial Vehicle Image Using a Convolutional Neural Network
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[4] YOLOX on Embedded Device With CCTV & TensorRT for Intelligent Multicategories Garbage Identification and Classification
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[5] Target Detection and Adaptive Tracking Based on Multisensor Data Fusion in a Smoke Environment
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[6] Gas Cylinder Detection Using Deep Learning Based YOLOv5 Object Detection Method
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[7] A Moving Ferromagnetic Target Localization Method Based on YOLO-V3
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[8] UAV Based Vehicle Detection on Real and Synthetic Image Pairs: Performance Differences and Influence Analysis of Context and Simulation Parameters
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[9] Application of vehicle detection with YOLOv3 in road surveillance video
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[11] Moving Image YOLOv3 Process Comparison with Multiple Resolutions and Frames-per-Second
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[12] A Review of Yolo algorithm developments
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[13] Remote sensing image super-resolution and object detection: Benchmark and state of the art
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[14] Automatic target detection from satellite imagery using machine learning
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[15] SHAP-Based Interpretable Object Detection Method for Satellite Imagery
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[16] Research on Deep Learning Automatic Vehicle Recognition Algorithm Based on RES-YOLO Model
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[17] SIMPL: Generating Synthetic Overhead Imagery to Address Custom Zero-Shot and Few-Shot Detection Problems
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[18] Intelligent Detection of Hazardous Goods Vehicles and Determination of Risk Grade Based on Deep Learning
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[19] Analysis of the influence of de-hazing methods on vehicle detection in aerial images
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[20] Attacking Object Detector Using A Universal Targeted Label-Switch Patch
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[21] Fully connected network with multi-scale dilation convolution module in evaluating atrial septal defect based on MRI segmentation
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[22] Comparison of two deep learning methods for detecting fire hotspots.
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[23] Detection of aircraft, vehicles and ships in aerial and satellite imagery using evolutionary deep learning
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[24] Vehicle Detection From UAV Imagery With Deep Learning: A Review
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[25] Context-Dependent Deep Learning
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[26] Railway Tracks Defects Detection Based on Deep Convolution Neural Networks
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[27] Goaling recognition based on intelligent analysis of real-time basketball image of Internet of Things
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[28] A Review: Object Detection Models
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[29] Automatic Vehicle Detection from Satellite Images Using Deep Learning Algorithm
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[30] Rapid Detection of Aircrafts in Satellite Imagery based on Deep Neural Networks
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[31] RGDiNet: Efficient Onboard Object Detection with Faster R-CNN for Air-to-Ground Surveillance
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[32] SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and Few-Shot Detection Problems
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[33] Robust vehicle detection under different weather conditions
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[34] YOLO 신경망 기반의 UAV 영상을 이용한 건물 객체 탐지 분석
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[35] Optimized EfficientDet deep learning model for vehicle detection
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[36] Aerial Dataset Integration For Vehicle Detection Based on YOLOv4
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[37] Multi-lane vehicle counting based on DCN-Mobile-YOLO model
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[38] Navigation and Task Planning of a Mobile Robot under ROS Environment: A Case Study Using AutoRace Challenge
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[39] Analysis of Building Object Detection Based on the YOLO Neural Network Using UAV Images
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[40] Detection of Strategic Targets of Interest in Satellite Images using YOLO
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[41] DETEKSI JALAN DI SEKITAR AUTONOMOUS ELECTRIC VEHICLE BERBASIS ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN)
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[42] Invariant Feature based Darknet Architecture for Moving Object classification
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[43] Identification of Urban Functional Areas by Coupling Satellite Images and Taxi GPS Trajectories
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[44] Grammar Guided Genetic Programming for Network Architecture Search and Road Detection on Aerial Orthophotography
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[45] Optimizing the Recognition and Feature Extraction of Wind Turbines through Hybrid Semantic Segmentation Architectures
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[46] YOLO-based Tricycle Detection from Traffic Video
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[47] Object Detection Using Stacked YOLOv3 Object Detection Using Stacked YOLOv3
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[48] Vision‐based vehicle detection for road traffic congestion classification
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[49] Two-Wheeled Vehicle Detection Using Two-Step and Single-Step Deep Learning Models
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[50] Non-anchor-based vehicle detection for traffic surveillance using bounding ellipses
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[51] Deep Learning-Based Segmentation of Key Objects of Transmission Lines
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[52] Vehicle Density Analysis and Classification using YOLOv3 for Smart Cities
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[53] Design and Implementation of a Video/Voice Process System for Recognizing Vehicle Parts Based on Artificial Intelligence
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[54] Object Detection Using Stacked YOLOv3.
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[55] VAID: An Aerial Image Dataset for Vehicle Detection and Classification
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[56] 無人飛行載具空拍影像目標物件即時自動辨識與定位系統之研究
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[57] Deep Learning Algorithms for Vehicle Detection on UAV Platforms: First Investigations on the Effects of Synthetic Training
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[58] Real-Time Vehicle Detection from Captured Images
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[59] Freight Analysis Using YOLOv2
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[60] Fire Hotspots Detection System on CCTV Videos Using You Only Look Once (YOLO) Method and Tiny YOLO Model for High Buildings Evacuation
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[61] Comparative Analysis and Implementation of Different Human Detection Techniques
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[62] Deep learning-based vehicle detection from orthophoto and spatial accuracy analysis
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