Simultaneously Detecting and Counting Dense Vehicles From Drone Images

Li, Wei; Li, Hongliang; Wu, Qingbo; Chen, Xiaoyu; Ngan, King Ngi

doi:10.1109/tie.2019.2899548

Cited by 84 publications

(48 citation statements)

References 41 publications

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“…Resnet-101 model [ 3 ] is selected as the backbone network instead of the VGG-16 model. Many papers have revealed that the network depth is important for performance improvement, and some nontrivial visual detection tasks have also greatly benefited from very deep models [ 23 , 24 ]. According to our experiments, Resnet-101 model performs better than VGG-16 in aerial images detection.…”

Section: Methodsmentioning

confidence: 99%

“…This model proposes an objective function, which can be optimized to carry out rotation invariant constraints and fisher discrimination on the generated CNN features. Hu et al focuses on the large variance of scales, and designs a scale-insensitive convolution neural network which accomplishes by a context-aware RoI pooling and a multi-branch decision network [ 24 ]. Ju et al proposed a specially designed network for small object detection [ 25 ].…”

Section: Related Workmentioning

confidence: 99%

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An Improved FBPN-Based Detection Network for Vehicles in Aerial Images

Wang

2020

Sensors

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With the development of artificial intelligence and big data analytics, an increasing number of researchers have tried to use deep-learning technology to train neural networks and achieved great success in the field of vehicle detection. However, as a special domain of object detection, vehicle detection in aerial images still has made limited progress because of low resolution, complex backgrounds and rotating objects. In this paper, an improved feature-balanced pyramid network (FBPN) has been proposed to enhance the network’s ability to detect small objects. By combining FBPN with modified faster region convolutional neural network (faster-RCNN), a vehicle detection framework for aerial images is proposed. The focal loss function is adopted in the proposed framework to reduce the imbalance between easy and hard samples. The experimental results based on the VEDIA, USCAS-AOD, and DOTA datasets show that the proposed framework outperforms other state-of-the-art vehicle detection algorithms for aerial images.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Improved FBPN-Based Detection Network for Vehicles in Aerial Images

Wang

2020

Sensors

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show abstract

“…Among which C j i is used for category determination, C j i = 1 is equivalent to classification correctness, otherwise, it is equal to 0, and P j i is the confidence degree. The confidence error and classification error are calculated by cross-entropy function, and x,ŷ,ŵ,ĥ,Ĉ,P is the corresponding predicted value as demonstrated in equations (5), (7), (8) and (9).…”

Section: Loss Functionmentioning

confidence: 99%

“…Generally speaking, it is from objects with large distances rather than sensors that small object cases in aerial images are derived. Therefore, object detection is more challenging for aerial images than natural images [8]- [11].…”

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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“…With the development of deep learning, much progress has been achieved recently. Recent methods deal with the crowd counting solution by convolutional neural networks based object detectors [7,22,32,33]. To further improve the detection and counting accuracy, the deep frameworks focus on discriminative feature representation of the objects.…”

Section: Introductionmentioning

confidence: 99%

Guided Attention Network for Object Detection and Counting on Drones

Cai

Zhang

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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Object detection and counting are related but challenging problems, especially for drone based scenes with small objects and cluttered background. In this paper, we propose a new Guided Attention network (GAnet) to deal with both object detection and counting tasks based on the feature pyramid. Different from the previous methods relying on unsupervised attention modules, we fuse different scales of feature maps by using the proposed weakly-supervised Background Attention (BA) between the background and objects for more semantic feature representation. Then, the Foreground Attention (FA) module is developed to consider both global and local appearance of the object to facilitate accurate localization. Moreover, the new data argumentation strategy is designed to train a robust model in the drone based scenes with various illumination conditions. Extensive experiments on three challenging benchmarks (i.e., UAVDT, CARPK and PUCPR+) show the state-of-the-art detection and counting performance of the proposed method compared with existing methods. Code can be found at https://isrc.iscas.ac.cn/gitlab/research/ganet.

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Simultaneously Detecting and Counting Dense Vehicles From Drone Images

Cited by 84 publications

References 41 publications

An Improved FBPN-Based Detection Network for Vehicles in Aerial Images

An Improved FBPN-Based Detection Network for Vehicles in Aerial Images

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

Guided Attention Network for Object Detection and Counting on Drones

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