An enhanced deep convolutional neural network for densely packed objects detection in remote sensing images

Deng, Zhipeng; Lei, Lin; Sun, Hao; Zou, Huanxin; Zhou, Shilin; Zhao, Jiewen

doi:10.1109/rsip.2017.7958800

Cited by 22 publications

(19 citation statements)

References 8 publications

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“…Furthermore, some researchers [12,32,33] also investigated whether fast R-CNN or faster R-CNN showed great performance in vehicle detection on aerial images. Deng [12] improved the traditional faster R-CNN for vehicle detection and orientation estimation for aerial images.…”

Section: Vehicle Detection Methods For Aerial Imagesmentioning

confidence: 99%

Arbitrary-Oriented Vehicle Detection in Aerial Imagery with Single Convolutional Neural Networks

et al. 2017

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Vehicle detection with orientation estimation in aerial images has received widespread interest as it is important for intelligent traffic management. This is a challenging task, not only because of the complex background and relatively small size of the target, but also the various orientations of vehicles in aerial images captured from the top view. The existing methods for oriented vehicle detection need several post-processing steps to generate final detection results with orientation, which are not efficient enough. Moreover, they can only get discrete orientation information for each target. In this paper, we present an end-to-end single convolutional neural network to generate arbitrarily-oriented detection results directly. Our approach, named Oriented_SSD (Single Shot MultiBox Detector, SSD), uses a set of default boxes with various scales on each feature map location to produce detection bounding boxes. Meanwhile, offsets are predicted for each default box to better match the object shape, which contain the angle parameter for oriented bounding boxes' generation. Evaluation results on the public DLR Vehicle Aerial dataset and Vehicle Detection in Aerial Imagery (VEDAI) dataset demonstrate that our method can detect both the location and orientation of the vehicle with high accuracy and fast speed. For test images in the DLR Vehicle Aerial dataset with a size of 5616 × 3744, our method achieves 76.1% average precision (AP) and 78.7% correct direction classification at 5.17 s on an NVIDIA GTX-1060.

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Section: Vehicle Detection Methods For Aerial Imagesmentioning

confidence: 99%

Arbitrary-Oriented Vehicle Detection in Aerial Imagery with Single Convolutional Neural Networks

et al. 2017

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“…The activation function is generally nonlinear, which enables the network to be capable of learning on layer-wise nonlinear mapping. Common activation functions include Sigmoid, Rectified Linear Unit (ReLU) (Hara, Saito, & Shouno, 2015) and Maxout functions (Goodfellow, Warde-Farley, Mirza, Courville, & Bengio, 2013). A loss function, which is also referred to as a cost function or an objective function, is used to represent the extent of previous inconsistencies between the value predicted by the model and the actual value.…”

Section: Activation and Loss Functionsmentioning

confidence: 99%

“…Electrical devices detection in UAV images (Wang, Tian, Liu, Liu, & Li, 2017); Ships (Huang, Jiang, Zhang, & Yao, 2017); Palm trees (Li, Fu, et al, 2016); Golf courses (Ishii, Nakamura, Nakada, Mochizuki, and Ishikawa, 2015) Aircraft (Wu, Zhang, Zhang, & Xu, 2015) Aircraft (Deng et al, 2017; Aircraft (Yao, Wang, Huo, and Fang, 2017)…”

Section: Relatively Complex Training Processmentioning

confidence: 99%

A survey of remote sensing image classification based on CNNs

et al. 2019

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With the development of earth observation technologies, the acquired remote sensing images are increasing dramatically, and a new era of big data in remote sensing is coming. How to effectively mine these massive volumes of remote sensing data are new challenges. Deep learning provides a new approach for analyzing these remote sensing data. As one of the deep learning models, convolutional neural networks (CNNs) can directly extract features from massive amounts of imagery data and is good at exploiting semantic features of imagery data. CNNs have achieved remarkable success in computer vision. In recent years, quite a few researchers have studied remote sensing image classification using CNNs, and CNNs can be applied to realize rapid, economical and accurate analysis and feature extraction from remote sensing data. This paper aims to provide a survey of the current state-of-the-art application of CNN-based deep learning in remote sensing image classification. We first briefly introduce the principles and characteristics of CNNs. We then survey developments and structural improvements on CNN models that make CNNs more suitable for remote sensing image classification, available datasets for remote sensing image classification, and data augmentation techniques. Then, three typical CNN application cases in remote sensing image classification: scene classification, object detection and object segmentation are presented. We also discuss the problems and challenges of CNN-based remote sensing image classification, and propose corresponding measures and suggestions. We hope that the survey can facilitate the advancement of remote sensing image classification research and help remote-sensing scientists to tackle classification tasks with the state-of-art deep learning algorithms and techniques.

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“…The detection accuracy increased by an average of 3.66%. In [23], an enhancement of Faster R-CNN was introduced to detect densely packed objects in satellite images. Enormous experiments were conducted to evaluate the effectiveness of the proposed method in terms of accuracy and IOU.…”

Section: Introductionmentioning

confidence: 99%

Object Detection Using Adaptive Mask RCNN in Optical Remote Sensing Images

Mahmoud¹,

Mohamed²,

El-Khoribi³

et al. 2020

IJIES

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Fast and automatic object detection in remote sensing images is a critical and challenging task for civilian and military applications. Recently, deep learning approaches were introduced to overcome the limitation of traditional object detection methods. In this paper, adaptive mask Region-based Convolutional Network (mask-RCNN) is utilized for multi-class object detection in remote sensing images. Transfer learning, data augmentation, and fine-tuning were adopted to overcome objects scale variability, small size, the density of objects, and the scarcity of annotated remote sensing image. Also, five optimization methods were investigated namely: Adaptive Moment Estimation (Adam), stochastic gradient decent (SGD), adaptive learning rate method (Adelta), Root Mean Square Propagation (RMSprop) and hybrid optimization. In hybrid optimization, the training process begins Adam then switches to SGD when appropriate and vice versa. Also, the behaviour of adaptive mask RCNN was compared to baseline deep object detection methods. Several experiments were conducted on the challenging NWPU-VHR-10 dataset. The hybrid method Adam_SGD acheived the highest Accuracy precision, with 95%. Experimental results showed detection performance in terms of accuracy and intersection over union (IOU) boost of performance up to 6%.

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An enhanced deep convolutional neural network for densely packed objects detection in remote sensing images

Cited by 22 publications

References 8 publications

Arbitrary-Oriented Vehicle Detection in Aerial Imagery with Single Convolutional Neural Networks

Arbitrary-Oriented Vehicle Detection in Aerial Imagery with Single Convolutional Neural Networks

A survey of remote sensing image classification based on CNNs

Object Detection Using Adaptive Mask RCNN in Optical Remote Sensing Images

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