Research on the Passive Detection Model Based on 3D-HBF and FSVDD for Underwater High-Speed Small Targets and its Application

Due to the high-speed, short-time countermeasure and small target strength of underwater high-speed small targets (UHSST), it is difficult to use a traditional method to accurately detect UHSST. So a novel passive detection model based on three-dimensional hyperbeam forming (3D-HBF) and fuzzy support vector data description (FSVDD) is proposed, where these advantages of beam width reduction and side lobe suppression for 3D-HBF and excellent target-detection capability for FSVDD are combined. The model consists of two stages. In the first stage, 3D-HBF is carried out to obtain the beam respond vectors (BSV) from original underwater acoustic signals. In the second stage, the BSV are input into the detector based on FSVDD to detect and locate the underwater targets intelligently. This model is applied to target detection of UHSST, and these testing results show that the proposed model has better detection performance than the conventional beam forming method, with a high detection success rate and localization capability.

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A support vector data description approach to target detection in hyperspectral imagery

10.1117/12.818642 ◽

2009 ◽

Author(s):

Wesam A. Sakla ◽

Adel A. Sakla ◽

Andrew Chan

Keyword(s):

Target Detection ◽

Hyperspectral Imagery ◽

Support Vector ◽

Support Vector Data Description ◽

Vector Data ◽

Data Description

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Bacterial colonies detecting and counting based on enhanced CNN detection method

E3S Web of Conferences ◽

10.1051/e3sconf/202123302012 ◽

2021 ◽

Vol 233 ◽

pp. 02012

Author(s):

Shousheng Liu ◽

Zhigang Gai ◽

Xu Chai ◽

Fengxiang Guo ◽

Mei Zhang ◽

...

Keyword(s):

Target Detection ◽

Error Detection ◽

Detection Rate ◽

Detection Method ◽

Difficult Problem ◽

Detection Methods ◽

Detection Accuracy ◽

Detection Model ◽

Confidence Threshold ◽

Small Targets

Bacterial colonies detecting and counting is tedious and time-consuming work. Fortunately CNN (convolutional neural network) detection methods are effective for target detection. The bacterial colonies are a kind of small targets, which have been a difficult problem in the field of target detection technology. This paper proposes a small target enhancement detection method based on double CNNs, which can not only improve the detection accuracy, but also maintain the detection speed similar to the general detection model. The detection method uses double CNNs. The first CNN uses SSD_MOBILENET_V1 network with both target positioning and target recognition functions. The candidate targets are screened out with a low confidence threshold, which can ensure no missing detection of small targets. The second CNN obtains candidate target regions according to the first round of detection, intercepts image sub-blocks one by one, uses the MOBILENET_V1 network to filter out targets with a higher confidence threshold, which can ensure good detection of small targets. Through the two-round enhancement detection method has been transplanted to the embedded platform NVIDIA Jetson AGX Xavier, the detection accuracy of small targets is significantly improved, and the target error detection rate and missed detection rate are reduced to less than 1%.

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The Assisted Positioning Technology for High Speed Train Based on Deep Learning

Applied Sciences ◽

10.3390/app10238625 ◽

2020 ◽

Vol 10 (23) ◽

pp. 8625

Author(s):

Yali Song ◽

Yinghong Wen

Keyword(s):

Deep Learning ◽

High Speed ◽

Detection Method ◽

Generative Adversarial Networks ◽

Single Shot ◽

High Speed Train ◽

Data Set ◽

Detection Model ◽

Model Based ◽

Cumulative Error

In the positioning process of a high-speed train, cumulative error may result in a reduction in the positioning accuracy. The assisted positioning technology based on kilometer posts can be used as an effective method to correct the cumulative error. However, the traditional detection method of kilometer posts is time-consuming and complex, which greatly affects the correction efficiency. Therefore, in this paper, a kilometer post detection model based on deep learning is proposed. Firstly, the Deep Convolutional Generative Adversarial Networks (DCGAN) algorithm is introduced to construct an effective kilometer post data set. This greatly reduces the cost of real data acquisition and provides a prerequisite for the construction of the detection model. Then, by using the existing optimization as a reference and further simplifying the design of the Single Shot multibox Detector (SSD) model according to the specific application scenario of this paper, the kilometer post detection model based on an improved SSD algorithm is established. Finally, from the analysis of the experimental results, we know that the detection model established in this paper ensures both detection accuracy and efficiency. The accuracy of our model reached 98.92%, while the detection time was only 35.43 ms. Thus, our model realizes the rapid and accurate detection of kilometer posts and improves the assisted positioning technology based on kilometer posts by optimizing the detection method.

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