scholarly journals Water Level Estimation in Sewer Pipes Using Deep Convolutional Neural Networks

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3412
Author(s):  
Joakim Bruslund Haurum ◽  
Chris H. Bahnsen ◽  
Malte Pedersen ◽  
Thomas B. Moeslund
Keyword(s):  
Neural Networks ◽  
Water Level ◽  
Convolutional Neural Networks ◽  
Contextual Information ◽  
Ground Truth ◽  
Water Levels ◽  
Deep Convolutional Neural Networks ◽  
Regression Problem ◽  
Still Images ◽  
Visual Appearance

Sewer pipe inspections are currently conducted by professionals who remotely control a robot from above ground. This expensive and slow approach is prone to human mistakes. Therefore, there is both an economic and scientific interest in automating the inspection process by creating systems able to recognize sewer defects. However, the extent of research put into automatic water level estimation in sewers has been limited despite being a prerequisite for further analysis of the pipe as only sections above the water level can be visually inspected. In this work, we utilize a dataset of still images obtained from over 5000 inspections carried out for three different Danish water utilities companies. This dataset is used for training and testing decision tree methods and convolutional neural networks (CNNs) for automatic water level estimation. We pose the estimation problem as a classification and regression problem, and compare the results of both approaches. Furthermore, we compare the effect of using different inspection standards for labeling the ground truth water level. By treating the problem as a classification task and using the 2015 Danish sewer inspection standard, where water levels are clustered based on visual appearance, we achieve an averaged F1 score of 79.29% using a fine-tuned ResNet-50 CNN. This shows the potential of using CNNs for water level estimation. We believe including temporal and contextual information will improve the results further.

scholarly journals Sentiment Polarity Detection in Bengali Tweets Using Deep Convolutional Neural Networks

2019 ◽  
Vol 28 (3) ◽  
pp. 377-386 ◽  
Author(s):  
Kamal Sarkar
Keyword(s):  
Neural Networks ◽  
Sentiment Analysis ◽  
Convolutional Neural Networks ◽  
Early Stage ◽  
Contextual Information ◽  
Indian Languages ◽  
Deep Convolutional Neural Networks ◽  
Detection Model ◽  
Detection Systems ◽  
Hidden Layer

Abstract Sentiment polarity detection is one of the most popular sentiment analysis tasks. Sentiment polarity detection in tweets is a more difficult task than sentiment polarity detection in review documents, because tweets are relatively short and they contain limited contextual information. Although the amount of blog posts, tweets and comments in Indian languages is rapidly increasing on the web, research on sentiment analysis in Indian languages is at the early stage. In this paper, we present an approach that classifies the sentiment polarity of Bengali tweets using deep neural networks which consist of one convolutional layer, one hidden layer and one output layer, which is a soft-max layer. Our proposed approach has been tested on the Bengali tweet dataset released for Sentiment Analysis in Indian Languages contest 2015. We have compared the performance of our proposed convolutional neural networks (CNN)-based model with a sentiment polarity detection model that uses deep belief networks (DBN). Our experiments reveal that the performance of our proposed CNN-based system is better than our implemented DBN-based system and some existing Bengali sentiment polarity detection systems.

scholarly journals Binarization of music score with complex background by deep convolutional neural networks

2021 ◽  
Author(s):  
Minh-Trieu Tran ◽  
Quang-Nhat Vo ◽  
Guee-Sang Lee
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Feature Learning ◽  
Ground Truth ◽  
Learning Ability ◽  
Dense Layer ◽  
Deep Convolutional Neural Networks ◽  
Music Score ◽  
Complex Background ◽  
Network Backbone

AbstractBinarization is an important step for most of document analysis systems. Regarding music score images with a complex background, the existence of background clutters with a variety of shapes and colors creates many challenges for the binarization. This paper presents a model for binarization of the complex background music score images by fusion of deep convolutional neural networks. Our model is directly trained from image regions using pixel values as inputs and the binary ground truth as labels. By utilizing the generalization capability of the residual network backbone and useful feature learning ability of dense layer, the proposed network structures can differentiate foreground pixels from background clutters, minimize the possibility of overfitting phenomenon and thus can deal with complex background noises appearing in the music score images. Comparing to traditional algorithms, binary images generated by our method have a cleaner background and better-preserved strokes. The experiments with captured and synthetic music score images show promising results compared to existing methods.

scholarly journals Social Video Advertisement Replacement and its Evaluation in Convolutional Neural Networks

2021 ◽  
Vol 20 (1) ◽  
pp. 117-136
Author(s):  
Cheng Yang ◽  
Xiang Yu ◽  
Arun Kumar ◽  
G.G. Md. Nawaz Ali ◽  
Peter Han Joo Chong ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Evaluation Method ◽  
Learning Algorithm ◽  
Ground Truth ◽  
Recurrent Network ◽  
Human Being ◽  
Deep Convolutional Neural Networks ◽  
Deep Learning Algorithm ◽  
Curve Fitting Method

This paper introduces a method to use deep convolutional neural networks (CNNs) to automatically replace advertisement (AD) photo on social (or self-media) videos and provides the suitable evaluation method to compare different CNNs. An AD photo can replace a picture inside a video. However, if a human being occludes the replaced picture in the original video, the newly pasted AD photo will block the human occluded part. The deep learning algorithm is implemented to segment the human being from the video. The segmented human pixels are then pasted back to the occluded area, so that the AD photo replacement becomes natural and perfect appearance in the video. This process requires the predicted occlusion edge to be closed to the ground truth occlusion edge, so that the AD photo can be occluded naturally. Therefore, this research introduces a curve fitting method to measure the predicted occlusion edge’s error. By using this method, three CNN methods are applied and compared for the AD replacement. They are mask of regions convolutional neural network (Mask RCNN), a recurrent network for video object segmentation (ROVS) and DeeplabV3. The experimental results show the comparative segmentation accuracy of the different models and DeeplabV3 shows the best performance.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

scholarly journals Deep convolutional neural networks for cardiovascular vulnerable plaque detection

2019 ◽  
Vol 277 ◽  
pp. 02024 ◽  
Author(s):  
Lincan Li ◽  
Tong Jia ◽  
Tianqi Meng ◽  
Yizhe Liu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Vulnerable Plaque ◽  
Recall Rate ◽  
Superior Performance ◽  
Learning Approaches ◽  
Deep Convolutional Neural Networks ◽  
Vulnerable Plaques ◽  
Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

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