Ship recognition based on Hu invariant moments and convolutional neural network for video surveillance

Ren, Yongmei; Zhang, Qingnian; Guo, Zhiqiang

doi:10.1007/s11042-020-09574-2

Cited by 25 publications

(10 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The moment feature mainly expresses the geometric features in the image area. It has invariant characteristics such as rotation, translation, and scale, and is often called an invariant moment [38,39]. The moment invariant function has been widely used in image pattern recognition, classification, target recognition and other tasks.…”

Section: (4) Other Handcrafted Texture Extraction Methodsmentioning

confidence: 99%

Research on the Auxiliary Classification and Diagnosis of Lung Cancer Subtypes Based on Histopathological Images

Chen

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Lung cancer (LC) is one of the most serious cancers threatening human health. Histopathological examination is the gold standard for qualitative and clinical staging of lung tumors. However, the process for doctors to examine thousands of histopathological images is very cumbersome, especially for doctors with less experience. Therefore, objective pathological diagnosis results can effectively help doctors choose the most appropriate treatment mode, thereby improving the survival rate of patients. For the current problem of incomplete experimental subjects in the computer-aided diagnosis of lung cancer subtypes, this study included relatively rare lung adenosquamous carcinoma (ASC) samples for the first time, and proposed a computer-aided diagnosis method based on histopathological images of ASC, lung squamous cell carcinoma (LUSC) and small cell lung carcinoma (SCLC). Firstly, the multidimensional features of 121 LC histopathological images were extracted, and then the relevant features (Relief) algorithm was used for feature selection. The support vector machines (SVMs) classifier was used to classify LC subtypes, and the receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to make it more intuitive evaluate the generalization ability of the classifier. Finally, through a horizontal comparison with a variety of mainstream classification models, experiments show that the classification effect achieved by the Relief-SVM model is the best. The LUSC-ASC classification accuracy was 73.91%, the LUSC-SCLC classification accuracy was 83.91% and the ASC-SCLC classification accuracy was 73.67%. Our experimental results verify the potential of the auxiliary diagnosis model constructed by machine learning (ML) in the diagnosis of LC.

show abstract

Section: (4) Other Handcrafted Texture Extraction Methodsmentioning

confidence: 99%

Research on the Auxiliary Classification and Diagnosis of Lung Cancer Subtypes Based on Histopathological Images

Chen

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…It uses Gaussian Mixture Wasserstein GAN with gradient penalty to generate sufficient informative artificial samples of small ships and uses raw and generated data to approach high accuracy tiny object detection. Ren et al [72] proposed an effective ship image recognition method, which combines Hu invariant moment features and CNN features to achieve superior ship image recognition. Hu moment invariant feature joint to the last pooling layer achieves the highest recognition accuracy on self-built and VAIS datasets.…”

Section: Optimization Methodsmentioning

confidence: 99%

Survey on Deep Learning-Based Marine Object Detection

Zhang

et al. 2021

Journal of Advanced Transportation

View full text Add to dashboard Cite

We present a survey on marine object detection based on deep neural network approaches, which are state-of-the-art approaches for the development of autonomous ship navigation, maritime surveillance, shipping management, and other intelligent transportation system applications in the future. The fundamental task of maritime transportation surveillance and autonomous ship navigation is to construct a reachable visual perception system that requires high efficiency and high accuracy of marine object detection. Therefore, high-performance deep learning-based algorithms and high-quality marine-related datasets need to be summarized. This survey focuses on summarizing the methods and application scenarios of maritime object detection, analyzes the characteristics of different marine-related datasets, highlights the marine detection application of the YOLO series model, and also discusses the current limitations of object detection based on deep learning and possible breakthrough directions. The large-scale, multiscenario industrialized neural network training is an indispensable link to solve the practical application of marine object detection. A widely accepted and standardized large-scale marine object verification dataset should be proposed.

show abstract

“…Wang et al focused on feature extraction in their review of deep network models for maritime target recognition, which provides a valuable reference for research in this field [51]. For feature extraction, introducing orthogonal moment methods, such as Zemike moment [52] and Hu moment [53], for feature extraction and fusing them with features extracted by CNN can significantly improve the accuracy of ship recognition. Resnet-50, VGG-16, and DenseNet-121 were used as feature extractors on the Boat Re-ID dataset built by Spagnolof et al [54], and the results show that ResNet-50 achieved the best results among the three.…”

Section: Ship Recognition and Re-identificationmentioning

confidence: 99%

Marine Vision-Based Situational Awareness Using Discriminative Deep Learning: A Survey

Qiao

Liu

et al. 2021

JMSE

View full text Add to dashboard Cite

The primary task of marine surveillance is to construct a perfect marine situational awareness (MSA) system that serves to safeguard national maritime rights and interests and to maintain blue homeland security. Progress in maritime wireless communication, developments in artificial intelligence, and automation of marine turbines together imply that intelligent shipping is inevitable in future global shipping. Computer vision-based situational awareness provides visual semantic information to human beings that approximates eyesight, which makes it likely to be widely used in the field of intelligent marine transportation. We describe how we combined the visual perception tasks required for marine surveillance with those required for intelligent ship navigation to form a marine computer vision-based situational awareness complex and investigated the key technologies they have in common. Deep learning was a prerequisite activity. We summarize the progress made in four aspects of current research: full scene parsing of an image, target vessel re-identification, target vessel tracking, and multimodal data fusion with data from visual sensors. The paper gives a summary of research to date to provide background for this work and presents brief analyses of existing problems, outlines some state-of-the-art approaches, reviews available mainstream datasets, and indicates the likely direction of future research and development. As far as we know, this paper is the first review of research into the use of deep learning in situational awareness of the ocean surface. It provides a firm foundation for further investigation by researchers in related fields.

show abstract

Ship recognition based on Hu invariant moments and convolutional neural network for video surveillance

Cited by 25 publications

References 37 publications

Research on the Auxiliary Classification and Diagnosis of Lung Cancer Subtypes Based on Histopathological Images

Research on the Auxiliary Classification and Diagnosis of Lung Cancer Subtypes Based on Histopathological Images

Survey on Deep Learning-Based Marine Object Detection

Marine Vision-Based Situational Awareness Using Discriminative Deep Learning: A Survey

Contact Info

Product

Resources

About