Research on Recognition and Location Method of Insulator in Infrared Image Based on Deep Learning

Abstract Infrared thermography technology is widely used in the thermal condition detection of insulators due to its advantages of non-contact, sensitive, online detection. To realize the automatic detection of the operating condition of insulators in complex environments, this paper proposes a method for the recognition and location of the insulator based on Region-based Fully Convolutional Networks (R-FCN). The model was trained and tested on the constructed insulator infrared data set, compared with the SSD model. The results showed that the R-FCN detecting insulators can not only accurately locate insulators, but have an AP (average precision) value as high as 89.2%. Therefore, the findings in this paper have verified that R-FCN has great advantages in the recognition and location of infrared images of insulators and has practical application value.

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One View Per City for Buildings Segmentation in Remote-Sensing Images via Fully Convolutional Networks: A Proof-of-Concept Study

Sensors ◽

10.3390/s20010141 ◽

2019 ◽

Vol 20 (1) ◽

pp. 141

Author(s):

Jianguang Li ◽

Wen Li ◽

Cong Jin ◽

Lijuan Yang ◽

Hui He

Keyword(s):

Remote Sensing ◽

Image Analysis ◽

Environmental Benefits ◽

Sustainable Urban Development ◽

Aerial Image ◽

Proof Of Concept ◽

Data Set ◽

Concept Study ◽

Convolutional Networks ◽

Fully Convolutional Networks

The segmentation of buildings in remote-sensing (RS) images plays an important role in monitoring landscape changes. Quantification of these changes can be used to balance economic and environmental benefits and most importantly, to support the sustainable urban development. Deep learning has been upgrading the techniques for RS image analysis. However, it requires a large-scale data set for hyper-parameter optimization. To address this issue, the concept of “one view per city” is proposed and it explores the use of one RS image for parameter settings with the purpose of handling the rest images of the same city by the trained model. The proposal of this concept comes from the observation that buildings of a same city in single-source RS images demonstrate similar intensity distributions. To verify the feasibility, a proof-of-concept study is conducted and five fully convolutional networks are evaluated on five cities in the Inria Aerial Image Labeling database. Experimental results suggest that the concept can be explored to decrease the number of images for model training and it enables us to achieve competitive performance in buildings segmentation with decreased time consumption. Based on model optimization and universal image representation, it is full of potential to improve the segmentation performance, to enhance the generalization capacity, and to extend the application of the concept in RS image analysis.

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Low-observable targets detection for autonomous vehicles based on dual-modal sensor fusion with deep learning approach

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407019859821 ◽

2019 ◽

Vol 233 (9) ◽

pp. 2270-2283

Author(s):

Keke Geng ◽

Wei Zou ◽

Guodong Yin ◽

Yang Li ◽

Zihao Zhou ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Autonomous Vehicles ◽

Deep Neural Network ◽

Infrared Imaging ◽

Infrared Image ◽

Complex Environments ◽

Infrared Images ◽

Night Time ◽

Perception System

Environment perception is a basic and necessary technology for autonomous vehicles to ensure safety and reliable driving. A lot of studies have focused on the ideal environment, while much less work has been done on the perception of low-observable targets, features of which may not be obvious in a complex environment. However, it is inevitable for autonomous vehicles to drive in environmental conditions such as rain, snow and night-time, during which the features of the targets are not obvious and detection models trained by images with significant features fail to detect low-observable target. This article mainly studies the efficient and intelligent recognition algorithm of low-observable targets in complex environments, focuses on the development of engineering method to dual-modal image (color–infrared images) low-observable target recognition and explores the applications of infrared imaging and color imaging for an intelligent perception system in autonomous vehicles. A dual-modal deep neural network is established to fuse the color and infrared images and detect low-observable targets in dual-modal images. A manually labeled color–infrared image dataset of low-observable targets is built. The deep learning neural network is trained to optimize internal parameters to make the system capable for both pedestrians and vehicle recognition in complex environments. The experimental results indicate that the dual-modal deep neural network has a better performance on the low-observable target detection and recognition in complex environments than traditional methods.

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Dark Spot Detection in SAR Images of Oil Spill Using Segnet

Applied Sciences ◽

10.3390/app8122670 ◽

2018 ◽

Vol 8 (12) ◽

pp. 2670 ◽

Cited By ~ 9

Author(s):

Hao Guo ◽

Guo Wei ◽

Jubai An

Keyword(s):

Oil Spill ◽

Semantic Segmentation ◽

Dark Spot ◽

Sar Images ◽

Data Set ◽

High Noise ◽

Oil Spill Detection ◽

Convolutional Networks ◽

Fully Convolutional Networks ◽

Spot Detection

Damping Bragg scattering from the ocean surface is the basic underlying principle of synthetic aperture radar (SAR) oil slick detection, and they produce dark spots on SAR images. Dark spot detection is the first step in oil spill detection, which affects the accuracy of oil spill detection. However, some natural phenomena (such as waves, ocean currents, and low wind belts, as well as human factors) may change the backscatter intensity on the surface of the sea, resulting in uneven intensity, high noise, and blurred boundaries of oil slicks or lookalikes. In this paper, Segnet is used as a semantic segmentation model to detect dark spots in oil spill areas. The proposed method is applied to a data set of 4200 from five original SAR images of an oil spill. The effectiveness of the method is demonstrated through the comparison with fully convolutional networks (FCN), an initiator of semantic segmentation models, and some other segmentation methods. It is here observed that the proposed method can not only accurately identify the dark spots in SAR images, but also show a higher robustness under high noise and fuzzy boundary conditions.

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Diurnal and nocturnal cloud segmentation of all-sky imager (ASI) images using enhancement fully convolutional networks

Atmospheric Measurement Techniques ◽

10.5194/amt-12-4713-2019 ◽

2019 ◽

Vol 12 (9) ◽

pp. 4713-4724

Author(s):

Chaojun Shi ◽

Yatong Zhou ◽

Bo Qiu ◽

Jingfei He ◽

Mu Ding ◽

...

Keyword(s):

Color Space ◽

Histogram Equalization ◽

Convolutional Network ◽

Data Set ◽

Convolutional Networks ◽

Fully Convolutional Networks ◽

Saturation Intensity ◽

Hsi Color Space ◽

Rgb Color Space ◽

Sky Imager

Abstract. Cloud segmentation plays a very important role in astronomical observatory site selection. At present, few researchers segment cloud in nocturnal all-sky imager (ASI) images. This paper proposes a new automatic cloud segmentation algorithm that utilizes the advantages of deep-learning fully convolutional networks (FCNs) to segment cloud pixels from diurnal and nocturnal ASI images; it is called the enhancement fully convolutional network (EFCN). Firstly, all the ASI images in the data set from the Key Laboratory of Optical Astronomy at the National Astronomical Observatories of Chinese Academy of Sciences (CAS) are converted from the red–green–blue (RGB) color space to hue saturation intensity (HSI) color space. Secondly, the I channel of the HSI color space is enhanced by histogram equalization. Thirdly, all the ASI images are converted from the HSI color space to RGB color space. Then after 100 000 iterative trainings based on the ASI images in the training set, the optimum associated parameters of the EFCN-8s model are obtained. Finally, we use the trained EFCN-8s to segment the cloud pixels of the ASI image in the test set. In the experiments our proposed EFCN-8s was compared with four other algorithms (OTSU, FCN-8s, EFCN-32s, and EFCN-16s) using four evaluation metrics. Experiments show that the EFCN-8s is much more accurate in cloud segmentation for diurnal and nocturnal ASI images than the other four algorithms.

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Image-Based Plant Disease Identification by Deep Learning Meta-Architectures

Plants ◽

10.3390/plants9111451 ◽

2020 ◽

Vol 9 (11) ◽

pp. 1451

Author(s):

Muhammad Hammad Saleem ◽

Sapna Khanchi ◽

Johan Potgieter ◽

Khalid Mahmood Arif

Keyword(s):

Deep Learning ◽

Plant Disease ◽

State Of The Art ◽

Mean Average Precision ◽

Single Shot ◽

Average Precision ◽

Crop Monitoring ◽

Convolutional Networks ◽

Fully Convolutional Networks ◽

Agricultural Applications

The identification of plant disease is an imperative part of crop monitoring systems. Computer vision and deep learning (DL) techniques have been proven to be state-of-the-art to address various agricultural problems. This research performed the complex tasks of localization and classification of the disease in plant leaves. In this regard, three DL meta-architectures including the Single Shot MultiBox Detector (SSD), Faster Region-based Convolutional Neural Network (RCNN), and Region-based Fully Convolutional Networks (RFCN) were applied by using the TensorFlow object detection framework. All the DL models were trained/tested on a controlled environment dataset to recognize the disease in plant species. Moreover, an improvement in the mean average precision of the best-obtained deep learning architecture was attempted through different state-of-the-art deep learning optimizers. The SSD model trained with an Adam optimizer exhibited the highest mean average precision (mAP) of 73.07%. The successful identification of 26 different types of defected and 12 types of healthy leaves in a single framework proved the novelty of the work. In the future, the proposed detection methodology can also be adopted for other agricultural applications. Moreover, the generated weights can be reused for future real-time detection of plant disease in a controlled/uncontrolled environment.

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Fully Convolutional Networks for Multisource Building Extraction From an Open Aerial and Satellite Imagery Data Set

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2018.2858817 ◽

2019 ◽

Vol 57 (1) ◽

pp. 574-586 ◽

Cited By ~ 54

Author(s):

Shunping Ji ◽

Shiqing Wei ◽

Meng Lu

Keyword(s):

Satellite Imagery ◽

Building Extraction ◽

Data Set ◽

Convolutional Networks ◽

Fully Convolutional Networks ◽

Imagery Data

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Off-Grid DOA Estimation Based on Circularly Fully Convolutional Networks (CFCN) Using Space-Frequency Pseudo-Spectrum

Sensors ◽

10.3390/s21082767 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2767

Author(s):

Wenqiong Zhang ◽

Yiwei Huang ◽

Jianfei Tong ◽

Ming Bao ◽

Xiaodong Li

Keyword(s):

Low Frequency ◽

Doa Estimation ◽

Classification Problem ◽

Training Data ◽

Model Parameters ◽

Data Set ◽

Convolutional Networks ◽

Fully Convolutional Networks ◽

Space Frequency ◽

Multi Classification

Low-frequency multi-source direction-of-arrival (DOA) estimation has been challenging for micro-aperture arrays. Deep learning (DL)-based models have been introduced to this problem. Generally, existing DL-based methods formulate DOA estimation as a multi-label multi-classification problem. However, the accuracy of these methods is limited by the number of grids, and the performance is overly dependent on the training data set. In this paper, we propose an off-grid DL-based DOA estimation. The backbone is based on circularly fully convolutional networks (CFCN), trained by the data set labeled by space-frequency pseudo-spectra, and provides on-grid DOA proposals. Then, the regressor is developed to estimate the precise DOAs according to corresponding proposals and features. In this framework, spatial phase features are extracted by the circular convolution calculation. The improvement in spatial resolution is converted to increasing the dimensionality of features by rotating convolutional networks. This model ensures that the DOA estimations at different sub-bands have the same interpretation ability and effectively reduce network model parameters. The simulation and semi-anechoic chamber experiment results show that CFCN-based DOA is superior to existing methods in terms of generalization ability, resolution, and accuracy.

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Diurnal and nocturnal cloud segmentation of ASI images using enhancement fully convolutional networks

10.5194/amt-2019-93 ◽

2019 ◽

Author(s):

Chaojun Shi ◽

Yatong Zhou ◽

Bo Qiu ◽

Jingfei He ◽

Mu Ding ◽

...

Keyword(s):

Color Space ◽

Histogram Equalization ◽

Data Set ◽

Convolutional Networks ◽

Fully Convolutional Networks ◽

Saturation Intensity ◽

Hsi Color Space ◽

Rgb Color Space ◽

Optical Astronomy ◽

Academy Of Sciences

Abstract. Cloud segmentation plays a very important role in the astronomical observatory site selection. At present, few researchers segment cloud in the nocturnal All Sky Imager (ASI) images. This paper proposes a new automatic cloud segmentation algorithm which utilizes the advantages of deep learning fully convolutional networks (FCN) to segment cloud pixels from diurnal and nocturnal ASI images, named enhancement fully convolutional networks (EFCN). Firstly, all the ASI images in the data set from the Key Laboratory of Optical Astronomy at the National Astronomical Observatories of Chinese Academy of Sciences (CAS) are converted from the red-green-blue (RGB) color space to hue-saturation-intensity (HSI) color space. Secondly, the channel of the HSI color space is enhanced by the histogram equalization. Thirdly, all the ASI images are converted from the HSI color space to RGB color space. Then after 100000 times iterative training based on the ASI images in the training set, the optimum associated parameters of the EFCN-8s model are obtained. Finally, we use the trained EFCN-8s to segment the cloud pixels of the ASI image in the test set. In the experiments our proposed EFCN-8s was compared with other four algorithms (OTSU, FCN-8s, EFCN-32s, and EFCN-16s) using four evaluation metrics. Experiments show that the EFCN-8s is much more accurate in the cloud segmentation for diurnal and nocturnal ASI images than other four algorithms.

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