Ship Type Classification by Convolutional Neural Networks with Auditory-Like Mechanisms

Shen, Sheng; Yang, Honghui; Yao, Xiaohui; Li, Junhao; Xu, Guanghui; Sheng, Meiping

doi:10.3390/s20010253

Cited by 38 publications

(13 citation statements)

References 23 publications

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“…Commonly used are synthetic aperture radar (SAR) images, by which ships can be classified based on their shape [15]. Similar research was described in [16,17], where superstructure scattering features were analyzed in the process of classification. Similarly, in [18], the idea of ship classification was solved by analyzing sound signals and removing the background sound of the sea.…”

Section: Related Workmentioning

confidence: 97%

Classification of Non-Conventional Ships Using a Neural Bag-Of-Words Mechanism

Połap¹,

Wlodarczyk–Sielicka

2020

Sensors

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The existing methods for monitoring vessels are mainly based on radar and automatic identification systems. Additional sensors that are used include video cameras. Such systems feature cameras that capture images and software that analyzes the selected video frames. Methods for the classification of non-conventional vessels are not widely known. These methods, based on image samples, can be considered difficult. This paper is intended to show an alternative way to approach image classification problems; not by classifying the entire input data, but smaller parts. The described solution is based on splitting the image of a ship into smaller parts and classifying them into vectors that can be identified as features using a convolutional neural network (CNN). This idea is a representation of a bag-of-words mechanism, where created feature vectors might be called words, and by using them a solution can assign images a specific class. As part of the experiment, the authors performed two tests. In the first, two classes were analyzed and the results obtained show great potential for application. In the second, the authors used much larger sets of images belonging to five vessel types. The proposed method indeed improved the results of classic approaches by 5%. The paper shows an alternative approach for the classification of non-conventional vessels to increase accuracy.

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Section: Related Workmentioning

confidence: 97%

Classification of Non-Conventional Ships Using a Neural Bag-Of-Words Mechanism

Połap¹,

Wlodarczyk–Sielicka

2020

Sensors

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“…classification and prediction of video sequences. The most crucial operator in a CNN is the convolution operation, which executes either 2D convolution or 3D convolution [12], [13], [14] to extract features from input images or video sequences [15].…”

Section: Related Workmentioning

confidence: 99%

SOPNet Method for the Fine-Grained Measurement and Prediction of Precipitation Intensity Using Outdoor Surveillance Cameras

Lin

Yang

2020

IEEE Access

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Surveillance cameras have been widely used in urban environments and are increasingly used in rural ones. Such cameras have mostly been used for security, but they can be applied to the problem of furnishing fine-grained measurements and predictions of precipitation intensity. In this study, we formulated a stacked order-preserving (OP) learning framework to train a network using time-series data. We constructed an OP module, which uses a three-dimensional (3D) convolution operation to extract features with spatial and temporal information and that are associated with ConvLSTM; this feature extraction is used to learn the short-term and OP time-series relationships between features. Furthermore, the OP modules are stacked to form a stacked OP network (SOPNet), which strengthens the relationship between features in long-term time-series image sequences. This SOPNet can be use to obtain fine-grained measurements and predictions of precipitation intensity from images captured by outdoor surveillance cameras. Our main contributions are threefold. First, the SOPNet strengthens the short-term and longterm time-series relationship between features. Second, the SOPNet simultaneously examines spatial and temporal information to measure and predict precipitation intensity. Third, we constructed a precipitation intensity database based on optical images captured by outdoor surveillance cameras. We experimentally evaluated our proposed architecture using our self-collected data set. We found that SOPNet yields better performance and greater accuracy relative to its well-known state-of-the-art counterparts with respect to various metrics.

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“…As a result, the system detects flying drones and provides their initial recognition to the operator. In [ 2 ], a model was proposed for ship type classification. The proposed complex neural architecture was based on a time convolutional layer model which helped to compare the extracted ship features.…”

Section: Contributionsmentioning

confidence: 99%