Identification of Bacterial Blight Resistant Rice Seeds Using Terahertz Imaging and Hyperspectral Imaging Combined With Convolutional Neural Network

In recent years, terahertz imaging systems and techniques have been developed and have gradually become a leading frontier field. With the advantages of low radiation and clothing-penetrable, terahertz imaging technology has been widely used for the detection of concealed weapons or other contraband carried on personnel at airports and other secure locations. This paper aims to detect these concealed items with deep learning method for its well detection performance and real-time detection speed. Based on the analysis of the characteristics of terahertz images, an effective detection system is proposed in this paper. First, a lots of terahertz images are collected and labeled as the standard data format. Secondly, this paper establishes the terahertz classification dataset and proposes a classification method based on transfer learning. Then considering the special distribution of terahertz image, an improved faster region-based convolutional neural network (Faster R-CNN) method based on threshold segmentation is proposed for detecting human body and other objects independently. Finally, experimental results demonstrate the effectiveness and efficiency of the proposed method for terahertz image detection.

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Hyperspectral imaging combined with convolutional neural network for outdoor detection of potato diseases

10.1109/iscipt53667.2021.00177 ◽

2021 ◽

Author(s):

Feng Zhang ◽

XinTing Li ◽

Shuang Qiu ◽

Jie Feng ◽

DaWen Wang ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Hyperspectral Imaging ◽

Potato Diseases

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Convolutional neural network target detection in hyperspectral imaging for maritime surveillance

International Journal of Advanced Robotic Systems ◽

10.1177/1729881419842991 ◽

2019 ◽

Vol 16 (3) ◽

pp. 172988141984299

Author(s):

Sara Freitas ◽

Hugo Silva ◽

José Miguel Almeida ◽

Eduardo Silva

Keyword(s):

Neural Network ◽

Anomaly Detection ◽

Convolutional Neural Network ◽

Unmanned Aerial Vehicles ◽

Hyperspectral Imaging ◽

Imaging System ◽

Spectral Angle Mapper ◽

Maritime Surveillance ◽

Aerial Vehicles ◽

Spectral Angle

This work addresses a hyperspectral imaging system for maritime surveillance using unmanned aerial vehicles. The objective was to detect the presence of vessels using purely spatial and spectral hyperspectral information. To accomplish this objective, we implemented a novel 3-D convolutional neural network approach and compared against two implementations of other state-of-the-art methods: spectral angle mapper and hyperspectral derivative anomaly detection. The hyperspectral imaging system was developed during the SUNNY project, and the methods were tested using data collected during the project final demonstration, in São Jacinto Air Force Base, Aveiro (Portugal). The obtained results show that a 3-D CNN is able to improve the recall value, depending on the class, by an interval between 27% minimum, to a maximum of over 40%, when compared to spectral angle mapper and hyperspectral derivative anomaly detection approaches. Proving that 3-D CNN deep learning techniques that combine spectral and spatial information can be used to improve the detection of targets classification accuracy in hyperspectral imaging unmanned aerial vehicles maritime surveillance applications.

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Variety Identification of Single Rice Seed Using Hyperspectral Imaging Combined with Convolutional Neural Network

Applied Sciences ◽

10.3390/app8020212 ◽

2018 ◽

Vol 8 (2) ◽

pp. 212 ◽

Cited By ~ 52

Author(s):

Zhengjun Qiu ◽

Jian Chen ◽

Yiying Zhao ◽

Susu Zhu ◽

Yong He ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Hyperspectral Imaging ◽

Variety Identification ◽

Rice Seed ◽

Single Rice

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Convolutional neural network model based on terahertz imaging for integrated circuit defect detections

Optics Express ◽

10.1364/oe.384146 ◽

2020 ◽

Vol 28 (4) ◽

pp. 5000 ◽

Cited By ~ 1

Author(s):

Qi Mao ◽

Yunlong Zhu ◽

Cixing Lv ◽

Yao Lu ◽

Xiaohui Yan ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Model ◽

Neural Network Model ◽

Integrated Circuit ◽

Terahertz Imaging ◽

Model Based

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Intelligent Identification of MoS2 Nanostructures with Hyperspectral Imaging by 3D-CNN

Nanomaterials ◽

10.3390/nano10061161 ◽

2020 ◽

Vol 10 (6) ◽

pp. 1161 ◽

Cited By ~ 1

Author(s):

Kai-Chun Li ◽

Ming-Yen Lu ◽

Hong Thai Nguyen ◽

Shih-Wei Feng ◽

Sofya B. Artemkina ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Hyperspectral Imaging ◽

2D Materials ◽

Image Resolution ◽

Superior Performance ◽

Color Contrast ◽

Synthesis Methods ◽

Specific Substrate ◽

3D Cnn

Increasing attention has been paid to two-dimensional (2D) materials because of their superior performance and wafer-level synthesis methods. However, the large-area characterization, precision, intelligent automation, and high-efficiency detection of nanostructures for 2D materials have not yet reached an industrial level. Therefore, we use big data analysis and deep learning methods to develop a set of visible-light hyperspectral imaging technologies successfully for the automatic identification of few-layers MoS2. For the classification algorithm, we propose deep neural network, one-dimensional (1D) convolutional neural network, and three-dimensional (3D) convolutional neural network (3D-CNN) models to explore the correlation between the accuracy of model recognition and the optical characteristics of few-layers MoS2. The experimental results show that the 3D-CNN has better generalization capability than other classification models, and this model is applicable to the feature input of the spatial and spectral domains. Such a difference consists in previous versions of the present study without specific substrate, and images of different dynamic ranges on a section of the sample may be administered via the automatic shutter aperture. Therefore, adjusting the imaging quality under the same color contrast conditions is unnecessary, and the process of the conventional image is not used to achieve the maximum field of view recognition range of ~1.92 mm2. The image resolution can reach ~100 nm and the detection time is 3 min per one image.

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Deep Feature Representation with Stacked Sparse Auto-Encoder and Convolutional Neural Network for Hyperspectral Imaging-Based Detection of Cucumber Defects

Transactions of the ASABE ◽

10.13031/trans.12214 ◽

2018 ◽

Vol 61 (2) ◽

pp. 425-436 ◽

Cited By ~ 5

Author(s):

Ziyi Liu ◽

Yong He ◽

Haiyan Cen ◽

Renfu Lu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Hyperspectral Imaging ◽

Defect Detection ◽

Hyperspectral Image ◽

Imaging System ◽

Feature Representation ◽

Inspection System ◽

Deep Feature ◽

Pickling Cucumbers

Abstract. It is challenging to achieve rapid and accurate processing of large amounts of hyperspectral image data. This research was aimed to develop a novel classification method by employing deep feature representation with the stacked sparse auto-encoder (SSAE) and the SSAE combined with convolutional neural network (CNN-SSAE) learning for hyperspectral imaging-based defect detection of pickling cucumbers. Hyperspectral images of normal and defective pickling cucumbers were acquired using a hyperspectral imaging system running at two conveyor speeds of 85 and 165 mm s-1. An SSAE model was developed to learn the feature representation from the preprocessed data and to perform five-class (normal, watery, split/hollow, shrivel, and surface defect) classification. To deal with a more complicated task for different types of surface defects (i.e., dirt/sand and gouge/rot classes) in six-class classification, a CNN-SSAE system was developed. The results showed that the CNN-SSAE system improved the classification performance, compared with the SSAE, with overall accuracies of 91.1% and 88.3% for six-class classification at the two conveyor speeds. Additionally, the average running time of the CNN-SSAE system for each sample was less than 14 ms, showing considerable potential for application in an automated on-line inspection system for cucumber sorting and grading. Keywords: Convolutional neural network, Defect detection, Hyperspectral imaging, Pickling cucumber, Representation learning, Stacked sparse auto-encoder.

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