Fast vehicle detection in UAV images

Tang, Tianyu; Deng, Zhipeng; Zhou, Shilin; Lei, Lin; Zou, Huanxin

doi:10.1109/rsip.2017.7958795

Cited by 53 publications

(26 citation statements)

References 15 publications

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“…Modern forms of ANN have been introduced, in particular Deep Learning (DL), which has already shown great performance in remote sensing applications such as scene classification, object detection, and semantic segmentation. In recent years, DL combined with UAV technology has been applied to many applications such as car detection [ 5 , 6 ], real-time scene understanding [ 7 ], location identification and core fire area segmentation [ 8 ], etc. DL can take advantage of spectral, textural, geometrical, and contextual features in classifying UAV images with much higher accuracy compared to object-based classification [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach on Building Detection from Unmanned Aerial Vehicle-Based Images in Riverbank Monitoring

Boonpook

Tan

et al. 2018

Sensors

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Buildings along riverbanks are likely to be affected by rising water levels, therefore the acquisition of accurate building information has great importance not only for riverbank environmental protection but also for dealing with emergency cases like flooding. UAV-based photographs are flexible and cloud-free compared to satellite images and can provide very high-resolution images up to centimeter level, while there exist great challenges in quickly and accurately detecting and extracting building from UAV images because there are usually too many details and distortions on UAV images. In this paper, a deep learning (DL)-based approach is proposed for more accurately extracting building information, in which the network architecture, SegNet, is used in the semantic segmentation after the network training on a completely labeled UAV image dataset covering multi-dimension urban settlement appearances along a riverbank area in Chongqing. The experiment results show that an excellent performance has been obtained in the detection of buildings from untrained locations with an average overall accuracy more than 90%. To verify the generality and advantage of the proposed method, the procedure is further evaluated by training and testing with another two open standard datasets which have a variety of building patterns and styles, and the final overall accuracies of building extraction are more than 93% and 95%, respectively.

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Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach on Building Detection from Unmanned Aerial Vehicle-Based Images in Riverbank Monitoring

Boonpook

Tan

et al. 2018

Sensors

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“…In reference [29], YOLOv2 is used to detect vehicles in unmanned aerial vehicle images, which is the beginning of successful use CNN regression-based in the city administration. References [30][31][32][33][34][35] explored vehicle detection using CNNs.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Fuel Truck Detection Algorithm Based on Deep Convolutional Neural Network

et al. 2020

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This paper presents a new approach by training and improving a convolutional neural network (CNN) based on You Only Look Once version 2 (YOLOv2) to efficiently detect fuel trucks from images in embedded systems. The proposed method considers the entire image area for strong object detection compared with existing methods that only focus on the image area where the class object exists to predict its probability to be in a class. The loss function for CNN is improved to enhance effective learning, especially when only a limited amount of data is available for training. The class probability can be learned by improving the loss function although the anchor boxes are not in the center of the target object. The learning process of the model can be in a limited range and achieve rapid convergence although the sizes of the initial anchor and target boundary boxes are different. Experimental results of various fuel truck images show the efficiency of the proposed approach under different detection scenarios of real fuel trucks. The detection rate of the proposed method is approximately 4% higher than the YOLOv2 object detection method. The proposed method is suitable to monitor long country borders using unmanned drones.

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“…These network architectures are explored and optimized for satellite datasets which increased computational costs. In the image datasets captured by UAVs [1], [4], [19], [22], objects are typically centered and occupy a fixed fraction in the pictures. These network structures are generally different from above methods in that they are able to reach an area of interest flexibly and take pictures with various levels of details.…”

Section: Related Workmentioning

confidence: 99%

“…As airborne cameras and remote sensing systems keep developing, it is more and more common for high-resolution aerial images that are captured by unmanned airborne vehicles (UAVs) and satellites to provide data for researchers. As a result, object detection in aerial images becomes an essential technique in the attempt to automatically obtain instance-level information [1], [2]. Those machine vision systems and algorithms based on object detection, widely used in many critical applications such as military reconnaissance and intelligent transportation, are often applied to collecting information about the areas surrounding an object.…”

mentioning

confidence: 99%

Robust Object Detection in Aerial Imagery Based on Multi-Scale Detector and Soft Densely Connected

Zhang

Liu

et al. 2020

IEEE Access

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Object detection in aerial images is vital for autonomous guidance, navigation and control, and situational awareness. However, there are still many challenges facing researchers in this filed, including the target scales, the perspectives in taking pictures, and the highly complex background. The present paper introduces a robust object detector which is optimized for handling with multi-scale objects and the overhead capturing perspective object instances in aerial images. Firstly, in the feature extraction stage, an effective multi-scale detector (MSD) is designed to search for objects with different scales in feature maps. After that, when detecting a small target from a cluttered background, both the shallow and deep layer features are densely connected by the deconvolution after tackling the issues of low dimensionality in deep layers and inadequate representation of small objects. In the experiments part, we analyze the impacts of the above mentioned components on the model and make a comparison between the method at issue and other state-of-the-art approaches on two publicly-available datasets captured by satellites and high-altitude UAVs. The results show that the proposed method, which is applicable to a wider range of aerial images, is more effective and robust. INDEX TERMS Object detection, aerial images, multi-scale detection, small object relative scale (ORS). I. INTRODUCTION

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Fast vehicle detection in UAV images

Cited by 53 publications

References 15 publications

A Deep Learning Approach on Building Detection from Unmanned Aerial Vehicle-Based Images in Riverbank Monitoring

A Deep Learning Approach on Building Detection from Unmanned Aerial Vehicle-Based Images in Riverbank Monitoring

Real-Time Fuel Truck Detection Algorithm Based on Deep Convolutional Neural Network

Robust Object Detection in Aerial Imagery Based on Multi-Scale Detector and Soft Densely Connected

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