Obstacle type recognition in visual images via dilated convolutional neural network for unmanned surface vehicles

2022 ◽  
pp. 1-18
Author(s):  
Binghua Shi ◽  
Yixin Su ◽  
Cheng Lian ◽  
Chang Xiong ◽  
Yang Long ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Autonomous Navigation ◽  
Deep Level ◽  
Recognition Performance ◽  
Visual Images ◽  
Reactive Control ◽  
Detection Techniques ◽  
Unmanned Surface Vehicles ◽  
Cluttered Environment

Abstract Recognition of obstacle type based on visual sensors is important for navigation by unmanned surface vehicles (USV), including path planning, obstacle avoidance, and reactive control. Conventional detection techniques may fail to distinguish obstacles that are similar in visual appearance in a cluttered environment. This work proposes a novel obstacle type recognition approach that combines a dilated operator with the deep-level features map of ResNet50 for autonomous navigation. First, visual images are collected and annotated from various different scenarios for USV test navigation. Second, the deep learning model, based on a dilated convolutional neural network, is set and trained. Dilated convolution allows the whole network to learn deep features with increased receptive field and further improves the performance of obstacle type recognition. Third, a series of evaluation parameters are utilised to evaluate the obtained model, such as the mean average precision (mAP), missing rate and detection speed. Finally, some experiments are designed to verify the accuracy of the proposed approach using visual images in a cluttered environment. Experimental results demonstrate that the dilated convolutional neural network obtains better recognition performance than the other methods, with an mAP of 88%.

scholarly journals A Study of Defect Detection Techniques for Metallographic Images

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5593 ◽  
Author(s):  
Wei-Hung Wu ◽  
Jen-Chun Lee ◽  
Yi-Ming Wang
Keyword(s):  
Neural Network ◽  
Defect Detection ◽  
Network Architecture ◽  
Recognition Performance ◽  
Metallographic Analysis ◽  
Automatic Identification ◽  
Detection Techniques ◽  
Multi Scale ◽  
Modified Method ◽  
Metallographic Images

Metallography is the study of the structure of metals and alloys. Metallographic analysis can be regarded as a detection tool to assist in identifying a metal or alloy, to evaluate whether an alloy is processed correctly, to inspect multiple phases within a material, to locate and characterize imperfections such as voids or impurities, or to find the damaged areas of metallographic images. However, the defect detection of metallography is evaluated by human experts, and its automatic identification is still a challenge in almost every real solution. Deep learning has been applied to different problems in computer vision since the proposal of AlexNet in 2012. In this study, we propose a novel convolutional neural network architecture for metallographic analysis based on a modified residual neural network (ResNet). Multi-scale ResNet (M-ResNet), the modified method, improves efficiency by utilizing multi-scale operations for the accurate detection of objects of various sizes, especially small objects. The experimental results show that the proposed method yields an accuracy of 85.7% (mAP) in recognition performance, which is higher than existing methods. As a consequence, we propose a novel system for automatic defect detection as an application for metallographic analysis.

scholarly journals Novel Convolutional Neural Network with Variational Information Bottleneck for P300 Detection

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 39
Author(s):  
Hongpeng Liao ◽  
Jianwu Xu ◽  
Zhuliang Yu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Experimental Results ◽  
Redundant Information ◽  
Promising Tool ◽  
Information Bottleneck

In the area of brain-computer interfaces (BCI), the detection of P300 is a very important technique and has a lot of applications. Although this problem has been studied for decades, it is still a tough problem in electroencephalography (EEG) signal processing owing to its high dimension features and low signal-to-noise ratio (SNR). Recently, neural networks, like conventional neural networks (CNN), has shown excellent performance on many applications. However, standard convolutional neural networks suffer from performance degradation on dealing with noisy data or data with too many redundant information. In this paper, we proposed a novel convolutional neural network with variational information bottleneck for P300 detection. Wiht the CNN architecture and information bottleneck, the proposed network termed P300-VIB-Net could remove the redundant information in data effectively. The experimental results on BCI competition data sets show that P300-VIB-Net achieves cutting-edge character recognition performance. Furthermore, the proposed model is capable of restricting the flow of irrelevant information adaptively in the network from perspective of information theory. The experimental results show that P300-VIB-Net is a promising tool for P300 detection.

scholarly journals An Improved Deep Convolutional Neural Network-Based Autonomous Road Inspection Scheme Using Unmanned Aerial Vehicles

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2764
Author(s):  
Syed-Ali Hassan ◽  
Tariq Rahim ◽  
Soo-Young Shin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Autonomous Navigation ◽  
Activation Function ◽  
Performance Model ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
Suggested Technique ◽  
Road Inspection ◽  
Fully Connected

Recent advancements in the field of machine learning (ML) provide opportunity to conduct research on autonomous devices for a variety of applications. Intelligent decision-making is a critical task for self-driving systems. An attempt is made in this study to use a deep learning (DL) approach for the early detection of road cracks, potholes, and the yellow lane. The accuracy is not sufficient after training with the default model. To enhance accuracy, a convolutional neural network (CNN) model with 13 convolutional layers, a softmax layer as an output layer, and two fully connected layers (FCN) are constructed. In order to achieve the deeper propagation and to prevent saturation in the training phase, mish activation is employed in the first 12 layers with a rectified linear unit (ReLU) activation function. The upgraded CNN model performs better than the default CNN model in terms of accuracy. For the varied situation, a revised and enriched dataset for road cracks, potholes, and the yellow lane is created. The yellow lane is detected and tracked in order to move the unmanned aerial vehicle (UAV) autonomously by following yellow lane. After identifying a yellow lane, the UAV performs autonomous navigation while concurrently detecting road cracks and potholes using the robot operating system within the UAV. The performance model is benchmarked using performance measures, such as accuracy, sensitivity, F1-score, F2-score, and dice-coefficient, which demonstrate that the suggested technique produces better outcomes.

scholarly journals Comparing bags of features, conventional convolutional neural network and AlexNet for fruit recognition

2019 ◽  
Vol 14 (1) ◽  
pp. 333 ◽  
Author(s):  
Nik Noor Akmal Abdul Hamid ◽  
Rabiatul Adawiya Razali ◽  
Zaidah Ibrahim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Operation Time ◽  
Machine Learning Techniques ◽  
Bag Of Features ◽  
Learning Techniques ◽  
Recognition Result ◽  
Different Types

This paper presents a comparative study between Bag of Features (BoF), Conventional Convolutional Neural Network (CNN) and Alexnet for fruit recognition.  Automatic fruit recognition can minimize human intervention in their fruit harvesting operations, operation time and harvesting cost.  On the other hand, this task is very challenging because of the similarities in shapes, colours and textures among various types of fruits. Thus, a robust technique that can produce good result is necessary. Due to the outstanding performance of deep learning like CNN and its pre-trained models like AlexNet in image recognition, this paper investigates the accuracy of conventional CNN, and Alexnet in recognizing thirty different types of fruits from a publicly available dataset.  Besides that, the recognition performance of BoF is also examined since it is one of the machine learning techniques that achieves good result in object recognition.   The experimental results indicate that all of these three techniques produce excellent recognition accuracy. Furthermore, conventional CNN achieves the fastest recognition result compared to BoF, and Alexnet.

Taylor-rider-based deep convolutional neural network for image forgery detection in 3D lighting environment

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
V. Vinolin ◽  
M. Sucharitha
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Optimization Algorithm ◽  
Digital Images ◽  
Deep Convolutional Neural Network ◽  
Distance Measures ◽  
Forgery Detection ◽  
Content Type ◽  
Detection Techniques ◽  
Human Faces

PurposeWith the advancements in photo editing software, it is possible to generate fake images, degrading the trust in digital images. Forged images, which appear like authentic images, can be created without leaving any visual clues about the alteration in the image. Image forensic field has introduced several forgery detection techniques, which effectively distinguish fake images from the original ones, to restore the trust in digital images. Among several forgery images, spliced images involving human faces are more unsafe. Hence, there is a need for a forgery detection approach to detect the spliced images.Design/methodology/approachThis paper proposes a Taylor-rider optimization algorithm-based deep convolutional neural network (Taylor-ROA-based DeepCNN) for detecting spliced images. Initially, the human faces in the spliced images are detected using the Viola–Jones algorithm, from which the 3-dimensional (3D) shape of the face is established using landmark-based 3D morphable model (L3DMM), which estimates the light coefficients. Then, the distance measures, such as Bhattacharya, Seuclidean, Euclidean, Hamming, Chebyshev and correlation coefficients are determined from the light coefficients of the faces. These form the feature vector to the proposed Taylor-ROA-based DeepCNN, which determines the spliced images.FindingsExperimental analysis using DSO-1, DSI-1, real dataset and hybrid dataset reveal that the proposed approach acquired the maximal accuracy, true positive rate (TPR) and true negative rate (TNR) of 99%, 98.88% and 96.03%, respectively, for DSO-1 dataset. The proposed method reached the performance improvement of 24.49%, 8.92%, 6.72%, 4.17%, 0.25%, 0.13%, 0.06%, and 0.06% in comparison to the existing methods, such as Kee and Farid's, shape from shading (SFS), random guess, Bo Peng et al., neural network, FOA-SVNN, CNN-based MBK, and Manoj Kumar et al., respectively, in terms of accuracy.Originality/valueThe Taylor-ROA is developed by integrating the Taylor series in rider optimization algorithm (ROA) for optimally tuning the DeepCNN.

Recognition of partial discharge of cable accessories based on convolutional neural network with small data set

2020 ◽  
Vol 39 (2) ◽  
pp. 431-446
Author(s):  
Anan Zhang ◽  
Jiahui He ◽  
Yu Lin ◽  
Qian Li ◽  
Wei Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recognition Performance ◽  
Partial Discharge ◽  
Measure Data ◽  
Small Data ◽  
Data Set ◽  
Content Type ◽  
Fault Recognition ◽  
Small Data Set

Purpose Considering the problem that the high recognition rate of deep learning requires the support of mass data, this study aims to propose an insulating fault identification method based on small data set convolutional neural network (CNN). Design/methodology/approach Because of the chaotic characteristics of partial discharge (PD) signals, the equivalent transformation of the PD signal of unit power frequency period is carried out by phase space reconstruction to derive the chaotic features. At the same time, geometric, fractal, entropy and time domain features are extracted to increase the volume of feature data. Finally, the combined features are constructed and imported into CNN to complete PD recognition. Findings The results of the case study show that the proposed method can realize the PD recognition of small data set and make up for the shortcomings of the methods based on CNN. Also, the 1-CNN built in this paper has better recognition performance for four typical insulation faults of cable accessories. The recognition performance is improved by 4.37% and 1.25%, respectively, compared with similar methods based on support vector machine and BPNN. Originality/value In this paper, a method of insulation fault recognition based on CNN with small data set is proposed, which can solve the difficulty to realize insulation fault recognition of cable accessories and deep data mining because of insufficient measure data.

Trajectory Planning and the Target Search by the Mobile Robot in an Environment Using a Behavior-Based Neural Network Approach

Robotica ◽  
2019 ◽  
Vol 38 (9) ◽  
pp. 1627-1641
Author(s):  
Krishna Kant Pandey ◽  
Dayal R. Parhi
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Target Position ◽  
Reactive Control ◽  
Neural Network Approach ◽  
Cluttered Environment ◽  
Reactive Behavior ◽  
Behavior Based ◽  
And Performance ◽  
Point Detection

SUMMARYNavigation and path analysis in a cluttered environment is a challenging task over the last few decades. In this paper, a behavior-based neural network (BNN) and reactive control architecture have been presented for navigation of the mobile robot. Two different reactive behaviors have been taken as inputs function. Obstacle position is the first reactive behavior given by u(o), whereas obstacle angle u(n) according to the target position is the second reactive behavior. The angular velocity and steering angle are the output of the controller. The backpropagation architecture reduces the errors of weight function and records the best weight data that match the BNN controller. Using the BNN algorithm, the robot reacts quickly as compared to other developed techniques. To validate the performance of the controller, simulation and experimental results have been compared in the common platforms. The deviation in results for both the scenarios is found to be within 10%. The results of the BNN algorithm have also been compared with other existing techniques. Effectiveness of the proposed technique is measured in terms of smoothness of the realistic path, collision point detection, path length, and performance time.

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