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A Semantic Segmentation Method for Road Environment Images Based on Hybrid convolutional auto-encoder

TRAITEMENT DU SIGNAL 2022年第4期39卷 1235-1245页

作者： Song, Xiaona Liu, Haichao Wang, Lijun Wang, Song Cao, Yunyu Xu, Donglai Zhang, Shenfeng North China Univ Water Resources & Elect Power Sch Mech Engn Zhengzhou 450011 Peoples R China Teesside Univ Sch Comp Engn & Digital Technol Middlesbrough TS1 3BX England Suzhou Tianshuo Intelligent Software Co Ltd Suzhou 215011 Peoples R China

Deep convolutional neural networks (CNNs) have presented amazing performance in the task of semantic segmentation. However, the network model is complex, the training time is prolonged, the semantic segmentation accuracy is not high and the real-time performance is not good, so it is difficult to be directly used in the semantic segmentation of road environment images of autonomous vehicles. As one of the three models of deep learning, the auto-encoder (AE) has powerful data learning and feature extracting capabilities from the raw data itself. In this study, the network architecture of auto-encoder and convolutional auto-encoder (CAE) is improved, supervised learning auto-encoder and improved convolutional auto-encoder are proposed, and a hybrid convolutional auto-encoder model is constructed by combining them. It can extract low-dimensional abstract features of road environment images by using convolution layers and pooling layers in front of the network, and then supervised learning auto-encoder are used to enhance and express semantic segmentation features, and finally de-convolution layers and un-pooling layers are used to generate semantic segmentation results. The hybrid convolutional auto-encoder model proposed in this paper not only contains encoding and decoding parts which are used in the common semantic segmentation models, but also adds semantic feature enhancing and representing parts, so that the network which has fewer convolutional and pooling layers can still achieve better semantic segmentation effects. Compared to the semantic segmentation based on convolutional neural networks, the hybrid convolutional auto -encoder has fewer network layers, fewer network parameters, and simpler network training. We evaluated our proposed method on Camvid and Cityscapes, which are standard benchmarks for semantic segmentation, and it proved to have a better semantic segmentation effect and good real-time performance.

关键词： semantic segmentation convolutional auto-encoder deep learning autonomous vehicles

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Research on the Fault Diagnosis Method of an Internal Gear Pump Based on a convolutional auto-encoder and PSO-LSSVM

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SENSORS 2022年第24期22卷 9841页

作者： Liao, Jian Zheng, Jianbo Chen, Zongbin Naval Univ Engn Inst Vibrat & Noise Wuhan 430033 Peoples R China Naval Univ Engn Naval Key Lab Ship Vibrat & Noise Wuhan 430033 Peoples R China

The raw signals produced by internal gear pumps are susceptible to noises brought on by mechanical vibrations and the surrounding environment, and the sample count collected during the various operating periods is not distributed evenly. Accurately diagnosing faults in internal gear pumps is significantly complicated by these factors. In light of these issues, accelerated life testing was performed in order to collect signals from an internal gear pump during various operating periods. Based on the architecture of a convolutional auto-encoder network, preprocessing of the signals in the various operating periods was performed to suppress noise and enhance operating period-representing features. Thereafter, variational mode decomposition was utilized to decompose the preprocessed signal into multiple intrinsic mode functions, and the multi-scale permutation entropy value was extracted for each intrinsic mode function to form a feature set. The feature set was subsequently divided into a training set and a test set, with the training set being trained to utilize a particle swarm optimization-least squares support vector machine network. For pattern recognition, the test set samples were fed into the trained model. The results demonstrated a 99.2% diagnostic accuracy. Compared to other methods of fault diagnosis, the proposed method is more effective and accurate.

关键词： internal gear pumps fault diagnosis convolutional auto-encoder multi-scale permutation entropy least squares support vector machine

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Image Geo-Site Estimation Using convolutional auto-encoder and Multi-Label Support Vector Machine

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INFORMATION 2023年第1期14卷 29页

作者： Jain, Arpit Verma, Chaman Kumar, Neerendra Raboaca, Maria Simona Baliya, Jyoti Narayan Suciu, George Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram 515001 Andhra Pradesh India Eotvos Lorand Univ Fac Informat Dept Media & Educ Informat H-1053 Budapest Hungary Cent Univ Jammu Dept Comp Sci & IT Jammu 181143 India Univ Politehn Bucuresti Doctoral Sch Splaiul Independentei St 313 Bucharest 060042 Romania Tech Univ Cluj Napoca Fac Bldg Serv Engn C-tin Daicoviciu St 15 Cluj Napoca 400020 Romania ICSI Energy Natl Res & Dev Inst Cryogen & Isotop T Ramnicu Valcea 240050 Romania Cent Univ Jammu Dept Educ Studies Jammu 181143 India R&D Dept Beia Consult Int Bucharest Bucharest 041386 Romania

The estimation of an image geo-site solely based on its contents is a promising task. Compelling image labelling relies heavily on contextual information, which is not as simple as recognizing a single object in an image. An auto-Encode-based support vector machine approach is proposed in this work to estimate the image geo-site to address the issue of misclassifying the estimations. The proposed method for geo-site estimation is conducted using a dataset consisting of 125 classes of various images captured within 125 countries. The proposed work uses a convolutional auto-Encode for training and dimensionality reduction. After that, the acquired preprocessed input dataset is further processed by a multi-label support vector machine. The performance assessment of the proposed approach has been accomplished using accuracy, sensitivity, specificity, and F1-score as evaluation parameters. Eventually, the proposed approach for image geo-site estimation presented in this article outperforms auto-Encode-based K-Nearest Neighbor and auto-Encode-Random Forest methods.

关键词： SVM classification convolutional auto-encoder auto-encoder-KNN algorithm random forest algorithm

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Application of a Hybrid Model Based on a convolutional auto-encoder and convolutional Neural Network in Object-Oriented Remote Sensing Classification

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ALGORITHMS 2018年第1期11卷 9-9页

作者： Cui, Wei Zhou, Qi Zheng, Zhendong Wuhan Univ Technol Resource & Environm Engn Coll Wuhan 430070 Hubei Peoples R China

Variation in the format and classification requirements for remote sensing data makes establishing a standard remote sensing sample dataset difficult. As a result, few remote sensing deep neural network models have been widely accepted. We propose a hybrid deep neural network model based on a convolutional auto-encoder and a complementary convolutional neural network to solve this problem. The convolutional auto-encoder supports feature extraction and data dimension reduction of remote sensing data. The extracted features are input into the convolutional neural network and subsequently classified. Experimental results show that in the proposed model, the classification accuracy increases from 0.916 to 0.944, compared to a traditional convolutional neural network model;furthermore, the number of training runs is reduced from 40,000 to 22,000, and the number of labelled samples can be reduced by more than half, all while ensuring a classification accuracy of no less than 0.9, which suggests the effectiveness and feasibility of the proposed model.

关键词： remote sensing classification object-oriented convolutional auto-encoder convolutional neural network

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Multi-Branch convolutional auto-encoder Model For Cross-Domain Person Re-Recognition

Multi-Branch Convolutional Auto-Encoder Model For Cross-Doma...

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第35届中国控制与决策会议

作者： Tao Luo Songhao Zhu College of Automation and Artificial Intelligence Nanjing University of Posts and Telecommunications

Cross-domain person re-recognition is very important for the applications of intelligent video *** further reduce the cross-domain differences,a new multi-branch convolutional auto-encoder based cross-domain person re-recognition network(MCAENet) is here ***,the reconstructed feature map is utilized as an input of the generative model module,and reconstruction loss and divergence loss are utilized to extract domain specific features and unknown domain features;then,the domain invariant feature guidance branch is introduced to enhance the robustness of domain invariant features;finally,identity loss is introduced and combined with the triple loss to constrain the distance between different pedestrians,so as to extract representative features to distinguish different *** experimental results demonstrate that the proposed cross-domain person re-recognition method based on convolutional-encoder achieves the best identification accuracy in various environments.

关键词： Person Re-Recognition convolutional auto-encoder Invariant Features Specific Feature

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Target Classification Using convolutional Deep Learning and auto-encoder Models 4

Target Classification Using Convolutional Deep Learning and ...

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4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Zaied, Sarra Toumi, Abdelmalek Khenchaf, Ali ENSTA Bretagne Lab STICC UMR CNRS 6285 2 Rue Francois Verny F-29806 Brest 9 France

ISBN: (纸本)9781538652398

Targets recognition in radar images presents an essential task for monitoring and surveillance of sensitive areas such as military zones. The fundamental problem in radar imaging is related to the recognition of objects in radar images, that its needs a whole chain of treatment. To classify radar images a feature extraction method is used to detect an appropriate subspace in the original feature space, which is based on transformation of the original feature This subspace should be big enough to maintain minimal loss of information and small enough to minimize the complexity of classifier. Since the feature extractor is difficult to build in manual mode and needs to be redesigned for each application, a Deep Learning in automatic mode is used with a training process subdivided into several modules. In this paper, we lay out an approach to classify Synthetic aperture radar (SAR) and Inverse Synthetic aperture radar (ISAR) images using Deep learning techniques. At first, in order to evaluate the effect of convolution layers and the number of hidden layers of the perceptron we thought of implementing 4 configurations of CNN (convolutional neural network). In the second time, we use the CAE (convolutional auto-encoder) to learn the optimal filters that minimize the reconstruction error, after we use these filters to feed the CNN retained and evaluate the effect on performance's system.

关键词： Target recognition Deep learning ISAR images SAR images convolutional neural network convolutional auto-encoder

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A study of deep convolutional auto-encoders for anomaly detection in videos

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PATTERN RECOGNITION LETTERS 2018年 105卷 13-22页

作者： Ribeiro, Manasses Lazzaretti, Andre Eugenio Lopes, Heitor Silverio Catarinense Fed Inst Educ Sci & Technol Rod SC 135 Km 125 BR-89560000 Videira SC Brazil Fed Univ Technol Parana UTFPR Av Sete Setembro 3165 BR-80230901 Curitiba PR Brazil

The detection of anomalous behaviors in automated video surveillance is a recurrent topic in recent computer vision research. Depending on the application field, anomalies can present different characteristics and challenges. convolutional Neural Networks have achieved the state-of-the-art performance for object recognition in recent years, since they learn features automatically during the training process. From the anomaly detection perspective, the convolutional autoencoder (CAE) is an interesting choice, since it captures the 2D structure in image sequences during the learning process. This work uses a CAE in the anomaly detection context, by applying the reconstruction error of each frame as an anomaly score. By exploring the CAE architecture, we also propose a method for aggregating high-level spatial and temporal features with the input frames and investigate how they affect the CAE performance. An easy-to-use measure of video spatial complexity was devised and correlated with the classification performance of the CAE. The proposed methods were evaluated by means of several experiments with public-domain datasets. The promising results support further research in this area. (c) 2017 Elsevier B.V. All rights reserved.

关键词： Deep learning convolutional auto-encoder Anomaly detection Object recognition Feature extraction

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N-light-N: A Highly-Adaptable Java Library for Document Analysis with convolutional auto-encoders and Related Architectures 15

<i>N-light-N</i>: A Highly-Adaptable Java Library for Docume...

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15th International Conference on Frontiers in Handwriting Recognition (ICFHR)

作者： Seuret, Mathias Ingold, Rolf Liwicki, Marcus Univ Fribourg Document Image & Voice Anal Grp Fribourg Switzerland

ISBN: (纸本)9781509009817

This paper presents a novel, highly-adaptable Java framework N-light-N, for the work with deep neural networks, especially with convolutional auto-encoders (CAE). While the most popular deep learning libraries focus on fast processing and high performance, they only implement the main-stream network architectures and network units. In recent research in the document domain, however, we have shown that modified networks, units, and training processes significantly improve the performance in various tasks. To enable the document research community with such capabilities, in this paper we introduce a novel, publicly available Deep Learning framework which is easy to use, adapt, and extend. Furthermore, we present successful applications for three tasks, including two in the domain of handwritten historical documents, and show how the framework can be used for adaptation, optimization, and deeper analysis.

关键词： convolutional auto-encoder Java framework layout analysis text detection

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Deep convolutional neural network based secure wireless voice communication for underground mines

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第10期12卷 9591-9610页

作者： Dey, Prasanjit Kumar, Chandan Mitra, Mitrabarun Mishra, Richa Chaulya, S. K. Prasad, G. M. Mandal, S. K. Banerjee, G. CSIR Cent Inst Min & Fuel Res Barwa Rd Dhanbad 826001 Bihar India

A secure wireless voice communication system for underground miners is an essential gadget for efficient and safe mining. Voice over internet protocol is a proven solution for wireless communication in underground mines where other cellular and satellite networks cannot be deployed. However, the wireless network's security is the major issue for the reliable operation of the system. A secure voice communication system has been developed by integrating voice over internet protocol system and deep convolutional neural network (DCNN) based trained model. Experimental results indicated that voice recognition accuracy of the DCNN based developed model was 93.7% for the noiseless environment. In contrast, it was 82.1 and 79% for the existing K-nearest-neighbour (KNN) and support vector machine (SVM) algorithms, respectively. Voice recognition response time of the DCNN, KNN, and SVM algorithms was 178, 220, and 228 ms, respectively. Thus, deployment of the developed secure and robust voice communication system would improve safety and productivity in underground mines.

关键词： convolutional auto-encoder Deep convolutional neural network Underground mine VoIP Wireless communication

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Topic driven multimodal similarity learning with multi-view voted convolutional features

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PATTERN RECOGNITION 2018年 75卷 223-234页

作者： Gao, Xinjian Mu, Tingting Goulermas, John Y. Wang, Meng Hefei Univ Technol Sch Comp & Informat Hefei Anhui Peoples R China Univ Manchester Sch Comp Sci Manchester M1 7DN Lancs England Univ Liverpool Dept Comp Sci Liverpool L69 3BX Merseyside England

Similarity (and distance metric) learning plays a very important role in many artificial intelligence tasks aiming at quantifying the relevance between objects. We address the challenge of learning complex relation patterns from data objects exhibiting heterogeneous properties, and develop an effective multi view multimodal similarity learning model with much improved learning performance and model interpretability. The proposed method firstly computes multi-view convolutional features to achieve improved object representation, then analyses the similarities between objects by operating over multiple hidden relation types (modalities), and finally fine-tunes the entire model variables via back -propagating a ranking loss to the convolutional layers. We develop a topic-driven initialization scheme, so that each learned relation type can be interpreted as a representative of semantic topics of the objects. To improve model interpretability and generalization, sparsity is imposed over these hidden relations. The proposed method is evaluated by solving the image retrieval task using challenging image datasets, and is compared with seven state-of-the-art algorithms in the field. Experimental results demonstrate significant performance improvement of the proposed method over the competing ones. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： convolutional auto-encoder Representation learning Multi-view learning Multimodal similarity learning

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