scholarly journals Generative Adversarial Network for Image Raindrop Removal of Transmission Line Based on Unmanned Aerial Vehicle Inspection

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Changbao Xu ◽  
Jipu Gao ◽  
Qi Wen ◽  
Bo Wang
Keyword(s):  
Detection Algorithm ◽  
Recurrent Network ◽  
Generative Adversarial Network ◽  
Coverage Area ◽  
Adversarial Network ◽  
Transmission Equipment ◽  
Physical Energy ◽  
Vehicle Inspection ◽  
Aerial Vehicle ◽  
Background Image

In the process of UAV line inspection, there may be raindrops on the camera lens. Raindrops have a serious impact on the details of the image, reducing the identification of the target transmission equipment in the image, reducing the accuracy of the target detection algorithm, and hindering the practicability of UAV line inspection technology in cyber-physical energy systems. In this paper, the principle of raindrop image formation is studied, and a method of raindrop removal based on generation countermeasure network is proposed. In this method, the attention recurrent network is used to generate the raindrop attention map, and the context code decoder is used to generate the raindrop image. The experimental results show that the proposed method can remove the raindrops in the image and repair the background image of raindrop coverage area and can generate a higher quality raindrop removal image than the traditional method.

scholarly journals Restoring Raindrops Using Attentive Generative Adversarial Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 7034
Author(s):  
Hee-Deok Yang
Keyword(s):  
Weather Conditions ◽  
Recurrent Network ◽  
Generative Adversarial Networks ◽  
Navigation Systems ◽  
Vision Systems ◽  
Generative Adversarial Network ◽  
Network Layers ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Outdoor Vision

Artificial intelligence technologies and vision systems are used in various devices, such as automotive navigation systems, object-tracking systems, and intelligent closed-circuit televisions. In particular, outdoor vision systems have been applied across numerous fields of analysis. Despite their widespread use, current systems work well under good weather conditions. They cannot account for inclement conditions, such as rain, fog, mist, and snow. Images captured under inclement conditions degrade the performance of vision systems. Vision systems need to detect, recognize, and remove noise because of rain, snow, and mist to boost the performance of the algorithms employed in image processing. Several studies have targeted the removal of noise resulting from inclement conditions. We focused on eliminating the effects of raindrops on images captured with outdoor vision systems in which the camera was exposed to rain. An attentive generative adversarial network (ATTGAN) was used to remove raindrops from the images. This network was composed of two parts: an attentive-recurrent network and a contextual autoencoder. The ATTGAN generated an attention map to detect rain droplets. A de-rained image was generated by increasing the number of attentive-recurrent network layers. We increased the number of visual attentive-recurrent network layers in order to prevent gradient sparsity so that the entire generation was more stable against the network without preventing the network from converging. The experimental results confirmed that the extended ATTGAN could effectively remove various types of raindrops from images.

scholarly journals AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5479 ◽  
Author(s):  
Maryam Rahnemoonfar ◽  
Jimmy Johnson ◽  
John Paden
Keyword(s):  
Data Augmentation ◽  
Synthetic Data ◽  
Detection Algorithm ◽  
Contour Detection ◽  
Training Data ◽  
Generative Adversarial Network ◽  
Radar Images ◽  
Adversarial Network ◽  
Radar Imagery

Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling process is the use of synthetically generated data with artificial intelligence. Instead of labeling real images, we can generate synthetic data based on arbitrary labels. In this way, training data can be quickly augmented with additional images. In this research, we evaluated the performance of synthetically generated radar images based on modified cycle-consistent adversarial networks. We conducted several experiments to test the quality of the generated radar imagery. We also tested the quality of a state-of-the-art contour detection algorithm on synthetic data and different combinations of real and synthetic data. Our experiments show that synthetic radar images generated by generative adversarial network (GAN) can be used in combination with real images for data augmentation and training of deep neural networks. However, the synthetic images generated by GANs cannot be used solely for training a neural network (training on synthetic and testing on real) as they cannot simulate all of the radar characteristics such as noise or Doppler effects. To the best of our knowledge, this is the first work in creating radar sounder imagery based on generative adversarial network.

scholarly journals HorNet: A Hierarchical Offshoot Recurrent Network for Improving Person Re-ID via Image Captioning

2019 ◽  
Author(s):  
Shiyang Yan ◽  
Jun Xu ◽  
Yuai Liu ◽  
Lin Xu
Keyword(s):  
State Of The Art ◽  
Recurrent Network ◽  
Image Captioning ◽  
Generative Adversarial Network ◽  
Visual Attributes ◽  
Adversarial Network ◽  
Language Representation ◽  
Benchmark Datasets ◽  
Similarity Preserving ◽  
Domain Transfer

Person re-identification (re-ID) aims to recognize a person-of-interest across different cameras with notable appearance variance. Existing research works focused on the capability and robustness of visual representation. In this paper, instead, we propose a novel hierarchical offshoot recurrent network (HorNet) for improving person re-ID via image captioning. Image captions are semantically richer and more consistent than visual attributes, which could significantly alleviate the variance. We use the similarity preserving generative adversarial network (SPGAN) and an image captioner to fulfill domain transfer and language descriptions generation. Then the proposed HorNet can learn the visual and language representation from both the images and captions jointly, and thus enhance the performance of person re-ID. Extensive experiments are conducted on several benchmark datasets with or without image captions, i.e., CUHK03, Market-1501, and Duke-MTMC, demonstrating the superiority of the proposed method. Our method can generate and extract meaningful image captions while achieving state-of-the-art performance.

scholarly journals Arrhythmia Detection Algorithm using GoogLeNet and Generative Adversarial Network with Lifelog Signals

2021 ◽  
Vol 15 ◽  
pp. 1-3
Author(s):  
Siho Shin ◽  
Jaehyo Jung ◽  
Mingu Kang ◽  
Youn Tae Kim
Keyword(s):  
Heart Attack ◽  
Detection Algorithm ◽  
Generative Adversarial Network ◽  
Arrhythmia Detection ◽  
Adversarial Network ◽  
Time Period ◽  
Short Period ◽  
Diagnosis Algorithm ◽  
Ecg Data

Arrhythmia is a cardiovascular disease with an irregular heartbeat, which can lead to a heart attack if it lasts for an excessive amount of time. Because the symptoms of arrhythmia occur irregularly, the heart needs to be monitored for a lengthy time period. This study suggests an arrhythmia diagnosis algorithm using GoogLeNet and a GAN. Because the algorithm proposed in this study can add to the number of data using a GAN, it can accurately diagnose an arrhythmic occurrence from measured lifelog over a short period of time. The classification of ECG data using GoogLeNet and a GAN showed an accuracy of approximately 99%.

scholarly journals A Nighttime Vehicle Detection Method with Attentive GAN for Accurate Classification and Regression

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1490
Author(s):  
Yan Liu ◽  
Tiantian Qiu ◽  
Jingwen Wang ◽  
Wenting Qi
Keyword(s):  
Region Of Interest ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Vital Role ◽  
Detection Accuracy ◽  
Local Regression ◽  
Generative Adversarial Network ◽  
Automatic Driving ◽  
Road Lighting ◽  
Adversarial Network

Vehicle detection plays a vital role in the design of Automatic Driving System (ADS), which has achieved remarkable improvements in recent years. However, vehicle detection in night scenes still has considerable challenges for the reason that the vehicle features are not obvious and are easily affected by complex road lighting or lights from vehicles. In this paper, a high-accuracy vehicle detection algorithm is proposed to detect vehicles in night scenes. Firstly, an improved Generative Adversarial Network (GAN), named Attentive GAN, is used to enhance the vehicle features of nighttime images. Then, with the purpose of achieving a higher detection accuracy, a multiple local regression is employed in the regression branch, which predicts multiple bounding box offsets. An improved Region of Interest (RoI) pooling method is used to get distinguishing features in a classification branch based on Faster Region-based Convolutional Neural Network (R-CNN). Cross entropy loss is introduced to improve the accuracy of classification branch. The proposed method is examined with the proposed dataset, which is composed of the selected nighttime images from BDD-100k dataset (Berkeley Diverse Driving Database, including 100,000 images). Compared with a series of state-of-the-art detectors, the experiments demonstrate that the proposed algorithm can effectively contribute to vehicle detection accuracy in nighttime.

scholarly journals IDENTIFYING EPIPHYTES IN DRONES PHOTOS WITH A CONDITIONAL GENERATIVE ADVERSARIAL NETWORK (C-GAN)

2020 ◽  
Vol XLIV-M-2-2020 ◽  
pp. 99-104
Author(s):  
A. Shashank ◽  
V. V. Sajithvariyar ◽  
V. Sowmya ◽  
K. P. Soman ◽  
R. Sivanpillai ◽  
...  
Keyword(s):  
Similarity Index ◽  
Structural Similarity ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Validation Data ◽  
Network Algorithms ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Aerial Vehicle ◽  
Index Measure

Abstract. Unmanned Aerial Vehicle (UAV) missions often collect large volumes of imagery data. However, not all images will have useful information, or be of sufficient quality. Manually sorting these images and selecting useful data are both time consuming and prone to interpreter bias. Deep neural network algorithms are capable of processing large image datasets and can be trained to identify specific targets. Generative Adversarial Networks (GANs) consist of two competing networks, Generator and Discriminator that can analyze, capture, and copy the variations within a given dataset. In this study, we selected a variant of GAN called Conditional-GAN that incorporates an additional label parameter, for identifying epiphytes in photos acquired by a UAV in forests within Costa Rica. We trained the network with 70%, 80%, and 90% of 119 photos containing the target epiphyte, Werauhia kupperiana (Bromeliaceae) and validated the algorithm’s performance using a validation data that were not used for training. The accuracy of the output was measured using structural similarity index measure (SSIM) index and histogram correlation (HC) coefficient. Results obtained in this study indicated that the output images generated by C-GAN were similar (average SSIM = 0.89–0.91 and average HC 0.97–0.99) to the analyst annotated images. However, C-GAN had difficulty to identify when the target plant was away from the camera, was not well lit, or covered by other plants. Results obtained in this study demonstrate the potential of C-GAN to reduce the time spent by botanists to identity epiphytes in images acquired by UAVs.

scholarly journals SharpGAN: Dynamic Scene Deblurring Method for Smart Ship Based on Receptive Field Block and Generative Adversarial Networks

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3641
Author(s):  
Hui Feng ◽  
Jundong Guo ◽  
Haixiang Xu ◽  
Shuzhi Sam Ge
Keyword(s):  
Receptive Field ◽  
Evaluation Criteria ◽  
Objective Evaluation ◽  
Detection Algorithm ◽  
Image Features ◽  
Motion Blur ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Blurred Image

Complex marine environment has an adverse effect on the object detection algorithm based on the vision sensor for the smart ship sailing at sea. In order to eliminate the motion blur in the images during the navigation of the smart ship and ensure safety, we propose SharpGAN, a new image deblurring method based on the generative adversarial network (GAN). First of all, we introduce the receptive field block net (RFBNet) to the deblurring network to enhance the network’s ability to extract blurred image features. Secondly, we propose a feature loss that combines different levels of image features to guide the network to perform higher-quality deblurring and improve the feature similarity between the restored images and the sharp images. Besides, we use the lightweight RFB-s module to significantly improve the real-time performance of the deblurring network. Compared with the existing deblurring methods, the proposed method not only has better deblurring performance in subjective visual effects and objective evaluation criteria, but also has higher deblurring efficiency. Finally, the experimental results reveal that the SharpGAN has a high correlation with the deblurring methods based on the physical model.

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