ShipRSImageNet: A Large-Scale Fine-Grained Dataset for Ship Detection in High-Resolution Optical Remote Sensing Images

Zhang, Zhengning; Zhang, Lin; Wang, Yue; Feng, Pengming; He, Ran

doi:10.1109/jstars.2021.3104230

Cited by 52 publications

(53 citation statements)

References 27 publications

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“…The computational complexity of the known neural network models [11][12][13][14][15][16][17][18] is one of their disadvantages. A solution to this problem can be found in the use of direct (Kronecker) penetrating product of matrices for the analytical description of operations performed in a concrete DCNN layer (expressions ( 10)-( 24)).…”

Section: Discussion Of the Results Obtained In The Study Of Recognizing The Object Images In Aerial Photographs Using Dcnnmentioning

confidence: 99%

“…Networks of direct data propagation [12][13][14][15] in which attribute values (the image) of the object being classified are fed to the input and a label (the class name) or a numeric class code is formed at the output are components of the DCNNs (CNNs) architecture most often used for classification. In the proposed model, an RGB image in JPEG format with a dimensionality of 227×227×3 is fed to the neural network input and a class label is attached at the output (Table 1).…”

Section: The Study Materials and Methodsmentioning

confidence: 99%

“…Detection of ships using remote sensing of optical images is discussed in [14]. It was shown that most of the existing detectors face difficulties in hard conditions when searching for and classifying small vessels.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Multiplication by a block-row and a block-vector of units 1 T and 1 actually formalizes the operation of summing the elements of the resulting matrix. As a result of the described operations, each block in the argument of the ReLU function in (14) turns into a scalar.…”

Section: Mw Kw P a W H B Mh Kh P A B (9)mentioning

confidence: 99%

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Improvement of the model of object recognition in aero photographs using deep convolutional neural networks

Slyusar

Protsenko

Chernukha

et al. 2021

EEJET

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Detection and recognition of objects in images is the main problem to be solved by computer vision systems. As part of solving this problem, the model of object recognition in aerial photographs taken from unmanned aerial vehicles has been improved. A study of object recognition in aerial photographs using deep convolutional neural networks has been carried out. Analysis of possible implementations showed that the AlexNet 2012 model (Canada) trained on the ImageNet image set (China) is most suitable for this problem solution. This model was used as a basic one. The object recognition error for this model with the use of the ImageNet test set of images amounted to 15 %. To solve the problem of improving the effectiveness of object recognition in aerial photographs for 10 classes of images, the final fully connected layer was modified by rejection from 1,000 to 10 neurons and additional two-stage training of the resulting model. Additional training was carried out with a set of images prepared from aerial photographs at stage 1 and with a set of VisDrone 2021 (China) images at stage 2. Optimal training parameters were selected: speed (step) (0.0001), number of epochs (100). As a result, a new model under the proposed name of AlexVisDrone was obtained. The effectiveness of the proposed model was checked with a test set of 100 images for each class (the total number of classes was 10). Accuracy and sensitivity were chosen as the main indicators of the model effectiveness. As a result, an increase in recognition accuracy from 7 % (for images from aerial photographs) to 9 % (for the VisDrone 2021 set) was obtained which has indicated that the choice of neural network architecture and training parameters was correct. The use of the proposed model makes it possible to automate the process of object recognition in aerial photographs. In the future, it is advisable to use this model at ground stations of unmanned aerial vehicle complex control when processing aerial photographs taken from unmanned aerial vehicles, in robotic systems, in video surveillance complexes and when designing unmanned vehicle systems

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Section: Discussion Of the Results Obtained In The Study Of Recognizing The Object Images In Aerial Photographs Using Dcnnmentioning

confidence: 99%

Section: The Study Materials and Methodsmentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Section: Mw Kw P a W H B Mh Kh P A B (9)mentioning

confidence: 99%

See 2 more Smart Citations

Improvement of the model of object recognition in aero photographs using deep convolutional neural networks

Slyusar

Protsenko

Chernukha

et al. 2021

EEJET

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show abstract

“…The resolution of the images ranged from 0.12m to 1.93m. ShipRSImageNet [267] is amongst the largest remotely sensed image datasets for fine-grained ship classifications which includes diverse complex environments and small ships. This makes this dataset suitable for deep learning-based methods.…”

Section: Generic Sod Datasetsmentioning

confidence: 99%

A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance

Rekavandi¹,

Liu²,

Boussaid³

et al. 2022

Preprint

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Small object detection (SOD) in optical images and videos is a challenging problem that even state-of-the-art generic object detection methods fail to accurately localize and identify such objects. Typically, small objects appear in real-world due to large camera-object distance. Because small objects occupy only a small area in the input image (e.g., less than 10%), the information extracted from such a small area is not always rich enough to support decision making. Multidisciplinary strategies are being developed by researchers working at the interface of deep learning and computer vision to enhance the performance of SOD deep learning based methods. In this paper, we provide a comprehensive review of over 160 research papers published between 2017 and 2022 in order to survey this growing subject. This paper summarizes the existing literature and provide a taxonomy that illustrates the broad picture of current research. We investigate how to improve the performance of small object detection in maritime environments, where increasing performance is critical. By establishing a connection between generic and maritime SOD research, future directions have been identified. In addition, the popular datasets that have been used for SOD for generic and maritime applications are discussed, and also well-known evaluation metrics for the state-of-the-art methods on some of the datasets are provided.

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Transfer Learning for On-Orbit Ship Segmentation

Fanizza¹,

Rijlaarsdam²,

González³

et al. 2023

Lecture Notes in Computer Science

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ShipRSImageNet: A Large-Scale Fine-Grained Dataset for Ship Detection in High-Resolution Optical Remote Sensing Images

Cited by 52 publications

References 27 publications

Improvement of the model of object recognition in aero photographs using deep convolutional neural networks

Improvement of the model of object recognition in aero photographs using deep convolutional neural networks

A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance

Transfer Learning for On-Orbit Ship Segmentation

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