scholarly journals Estimation of Wind Turbine Angular Velocity Remotely Found on Video Mining and Convolutional Neural Network

2020 ◽  
Vol 10 (10) ◽  
pp. 3544 ◽  
Author(s):  
Mahdi Bahaghighat ◽  
Qin Xin ◽  
Seyed Ahmad Motamedi ◽  
Morteza Mohammadi Zanjireh ◽  
Antoine Vacavant
Keyword(s):  
Deep Learning ◽  
Angular Velocity ◽  
Wind Turbine ◽  
Intelligent Systems ◽  
Wind Farm ◽  
Vision System ◽  
Detection Algorithm ◽  
Single Shot ◽  
Video Mining ◽  
Other Information

Today, energy issues are more important than ever. Because of the importance of environmental concerns, clean and renewable energies such as wind power have been most welcomed globally, especially in developing countries. Worldwide development of these technologies leads to the use of intelligent systems for monitoring and maintenance purposes. Besides, deep learning as a new area of machine learning is sharply developing. Its strong performance in computer vision problems has conducted us to provide a high accuracy intelligent machine vision system based on deep learning to estimate the wind turbine angular velocity, remotely. This velocity along with other information such as pitch angle and yaw angle can be used to estimate the wind farm energy production. For this purpose, we have used SSD (Single Shot Multi-Box Detector) object detection algorithm and some specific classification methods based on DenseNet, SqueezeNet, ResNet50, and InceptionV3 models. The results indicate that the proposed system can estimate rotational speed with about 99.05 % accuracy.

scholarly journals Robust Cherry Tomatoes Detection Algorithm in Greenhouse Scene Based on SSD

Agriculture ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 160
Author(s):  
Ting Yuan ◽  
Lin Lv ◽  
Fan Zhang ◽  
Jun Fu ◽  
Jin Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Detection Algorithm ◽  
Input Image ◽  
Detection Methods ◽  
Single Shot ◽  
Image Size ◽  
Growth Difference ◽  
Input Size ◽  
Base Network ◽  
Cherry Tomatoes

The detection of cherry tomatoes in greenhouse scene is of great significance for robotic harvesting. This paper states a method based on deep learning for cherry tomatoes detection to reduce the influence of illumination, growth difference, and occlusion. In view of such greenhouse operating environment and accuracy of deep learning, Single Shot multi-box Detector (SSD) was selected because of its excellent anti-interference ability and self-taught from datasets. The first step is to build datasets containing various conditions in greenhouse. According to the characteristics of cherry tomatoes, the image samples with illumination change, images rotation and noise enhancement were used to expand the datasets. Then training datasets were used to train and construct network model. To study the effect of base network and the input size of networks, one contrast experiment was designed on different base networks of VGG16, MobileNet, Inception V2 networks, and the other contrast experiment was conducted on changing the network input image size of 300 pixels by 300 pixels, 512 pixels by 512 pixels. Through the analysis of the experimental results, it is found that the Inception V2 network is the best base network with the average precision of 98.85% in greenhouse environment. Compared with other detection methods, this method shows substantial improvement in cherry tomatoes detection.

scholarly journals An Imbalance Fault Detection Algorithm for Variable-Speed Wind Turbines: A Deep Learning Approach

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2764 ◽  
Author(s):  
Jianjun Chen ◽  
Weihao Hu ◽  
Di Cao ◽  
Bin Zhang ◽  
Qi Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Detection ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Short Term Memory ◽  
Wind Farms ◽  
Detection Algorithm ◽  
Wind Turbine Blade ◽  
Mathematical Methods ◽  
Time Series Simulation

Wind power penetration has increased rapidly in recent years. In winter, the wind turbine blade imbalance fault caused by ice accretion increase the maintenance costs of wind farms. It is necessary to detect the fault before blade breakage occurs. Preliminary analysis of time series simulation data shows that it is difficult to detect the imbalance faults by traditional mathematical methods, as there is little difference between normal and fault conditions. A deep learning method for wind turbine blade imbalance fault detection and classification is proposed in this paper. A long short-term memory (LSTM) neural network model is built to extract the characteristics of the fault signal. The attention mechanism is built into the LSTM to increase its performance. The simulation results show that the proposed approach can detect the imbalance fault with an accuracy of over 98%, which proves the effectiveness of the proposed approach on wind turbine blade imbalance fault detection.

DEEP LEARNING TECHNIQUE - BASED DRONE DETECTION AND TRACKING

2021 ◽  
pp. 10-19
Author(s):  
Xuan Tung Truong
Keyword(s):  
Deep Learning ◽  
Detection Algorithm ◽  
Tracking Algorithm ◽  
Single Shot ◽  
Complex Environments ◽  
Security Risks ◽  
Security Breaches ◽  
Detection And Tracking ◽  
Illegal Activities ◽  
Fine Tune

The usage of small drones/UAVs is becoming increasingly important in recent years. Consequently, there is a rising potential of small drones being misused for illegal activities such as terrorism, smuggling of drugs, etc. posing high-security risks. Hence, tracking and surveillance of drones are essential to prevent security breaches. This paper resolves the problem of detecting small drones in surveillance videos using deep learning algorithms. Single Shot Detector (SSD) object detection algorithm and MobileNet-v2 architecture as the backbone were used for our experiments. The pre-trained model was re-trained on custom drone synthetic dataset by using transfer learning’s fine-tune technique. The results of detecting drone in our experiments were around 90.8%. The combination of drone detection, Dlib correlation tracking algorithm and centroid tracking algorithm effectively detects and tracks the small drone in various complex environments as well as is able to handle multiple target appearances.

scholarly journals Onboard Visual Horizon Detection for Unmanned Aerial Systems with Programmable Logic

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 614 ◽  
Author(s):  
Antal Hiba ◽  
Levente Márk Sántha ◽  
Tamás Zsedrovits ◽  
Levente Hajder ◽  
Akos Zarandy
Keyword(s):  
Vision System ◽  
Computational Cost ◽  
Detection Algorithm ◽  
Initial Guess ◽  
Unmanned Aircraft ◽  
Unmanned Aerial Systems ◽  
Single Shot ◽  
Camera Model ◽  
Radial Distortion ◽  
Horizon Detection

We introduce and analyze a fast horizon detection algorithm with native radial distortion handling and its implementation on a low power field programmable gate array (FPGA) development board in this paper. The algorithm is suited for visual applications in an airborne environment, that is on board a small unmanned aircraft. The algorithm was designed to have low complexity because of the power consumption requirements. To keep the computational cost low, an initial guess for the horizon is used, which is provided by the attitude heading reference system of the aircraft. The camera model takes radial distortions into account, which is necessary for a wide-angle lens used in most applications. This paper presents formulae for distorted horizon lines and a gradient sampling-based resolution-independent single shot algorithm for finding a horizon with radial distortion without undistortion of the complete image. The implemented algorithm is part of our visual sense-and-avoid system, where it is used for the sky-ground separation, and the performance of the algorithm is tested on real flight data. The FPGA implementation of the horizon detection method makes it possible to add this efficient module to any FPGA-based vision system.

scholarly journals Dimension Measurement and Key Point Detection of Boxes through Laser-Triangulation and Deep Learning-Based Techniques

2019 ◽  
Vol 10 (1) ◽  
pp. 26 ◽  
Author(s):  
Tao Peng ◽  
Zhijiang Zhang ◽  
Fansheng Chen ◽  
Dan Zeng
Keyword(s):  
Deep Learning ◽  
Vision System ◽  
Likelihood Estimation ◽  
Optimization Method ◽  
Detection Algorithm ◽  
Laser Triangulation ◽  
Box Dimension ◽  
Logistics Industry ◽  
Dimension Measurement ◽  
Point Detection

Dimension measurement is of utmost importance in the logistics industry. This work studies a hand-held structured light vision system for boxes. This system measures dimension information through laser triangulation and deep learning using only two laser-box images from a camera and a cross-line laser projector. The structured edge maps of the boxes are detected by a novel end-to-end deep learning model based on a trimmed-holistically nested edge detection network. The precise geometry of the box is calculated by the 3D coordinates of the key points in the laser-box image through laser triangulation. An optimization method for effectively calibrating the system through the maximum likelihood estimation is then proposed. Results show that the proposed key point detection algorithm and the designed laser-vision-based visual system can locate and perform dimension measurement of measured boxes with high accuracy and reliability. The experimental outcomes show that the system is suitable for portable automatic box dimension online measurement.

scholarly journals Early warning system for drivers’ phone usage with deep learning network

2021 ◽  
Author(s):  
Jixu Hou ◽  
Xiaofeng Xie ◽  
Qian Cai ◽  
Zhengjie Deng ◽  
Houqun Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Monitoring System ◽  
Warning System ◽  
Detection Algorithm ◽  
Single Shot ◽  
Intelligent Monitoring ◽  
Learning Network ◽  
Dangerous Driving ◽  
Deep Learning Network ◽  
Deep Learning Model

Abstract Dangerous driving, e.g., using mobile phone while driving, can result in serious traffic problem and threat to safely. To efficiently alleviate such problem, in this paper, we design a intelligent monitoring system to detect the dangerous behavior in driving. The monitoring system is combined by camera, terminal server, target detection algorithm and voice reminder. Furthermore, we applied an efficiently deep learning model, namely mobilenet combined with single shot multi-box detector (mobilenet-SSD), to identify the behavior of driver. To evaluate the performance of proposed system, we construct a dangerous driving dataset which consists of 6796 images. The experimental results show that the proposed system can achieve accuracy of 99% in 100 testing images. It can be used for real-time monitoring of the driver’s status.

A fast workpiece detection method based on multi-feature fused SSD

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Guoyuan Shi ◽  
Yingjie Zhang ◽  
Manni Zeng
Keyword(s):  
Deep Learning ◽  
Feature Fusion ◽  
Learning Algorithms ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Data Sets ◽  
Single Shot ◽  
Content Type ◽  
Sorting System ◽  
Study Designs

Purpose Workpiece sorting is a key link in industrial production lines. The vision-based workpiece sorting system is non-contact and widely applicable. The detection and recognition of workpieces are the key technologies of the workpiece sorting system. To introduce deep learning algorithms into workpiece detection and improve detection accuracy, this paper aims to propose a workpiece detection algorithm based on the single-shot multi-box detector (SSD). Design/methodology/approach Propose a multi-feature fused SSD network for fast workpiece detection. First, the multi-view CAD rendering images of the workpiece are used as deep learning data sets. Second, the visual geometry group network was trained for workpiece recognition to identify the category of the workpiece. Third, this study designs a multi-level feature fusion method to improve the detection accuracy of SSD (especially for small objects); specifically, a feature fusion module is added, which uses “element-wise sum” and “concatenation operation” to combine the information of shallow features and deep features. Findings Experimental results show that the actual workpiece detection accuracy of the method can reach 96% and the speed can reach 41 frames per second. Compared with the original SSD, the method improves the accuracy by 7% and improves the detection performance of small objects. Originality/value This paper innovatively introduces the SSD detection algorithm into workpiece detection in industrial scenarios and improves it. A feature fusion module has been added to combine the information of shallow features and deep features. The multi-feature fused SSD network proves the feasibility and practicality of introducing deep learning algorithms into workpiece sorting.

scholarly journals Single Shot Multibox Detector Automatic Polyp Detection Network Based on Gastrointestinal Endoscopic Images

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Xiaoling Chen ◽  
Kuiling Zhang ◽  
Shuying Lin ◽  
Kai Feng Dai ◽  
Yang Yun
Keyword(s):  
Deep Learning ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Single Shot ◽  
Polyp Detection ◽  
Endoscopic Images ◽  
Efficient Detection ◽  
Diagnosis Rate ◽  
Traditional Pattern ◽  
Fully Connected

Purpose. In order to resolve the situation of high missed diagnosis rate and high misdiagnosis rate of the pathological analysis of the gastrointestinal endoscopic images by experts, we propose an automatic polyp detection algorithm based on Single Shot Multibox Detector (SSD). Method. In the paper, SSD is based on VGG-16, the fully connected layer is changed to a convolutional layer, and four convolutional layers with successively decreasing scales are added as a new network structure. In order to verify the practicability, it is not only compared with manual polyp detection but also with Mask R-CNN. Results. Multiple experimental results show that the mean Average Precision (mAP) of the SSD network is 95.74%, which is 12.4% higher than the manual detection and 5.7% higher than the Mask R-CNN. When detecting a single frame of image, the detection speed of SSD is 8.41 times that of manual detection. Conclusion. Based on the traditional pattern recognition algorithm and the target detection algorithm using deep learning, we select a variety of algorithms to identify and classify polyps to achieve efficient detection results. Our research demonstrates that deep learning has a lot of room for development in the field of gastrointestinal image recognition.

scholarly journals Front Vehicle Detection Algorithm for Smart Car Based on Improved SSD Model

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4646 ◽  
Author(s):  
Jingwei Cao ◽  
Chuanxue Song ◽  
Shixin Song ◽  
Silun Peng ◽  
Da Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Network Model ◽  
Vehicle Detection ◽  
Weather Conditions ◽  
Detection Algorithm ◽  
Single Shot ◽  
Efficient Operation ◽  
Time Performance ◽  
Smart Cars

Vehicle detection is an indispensable part of environmental perception technology for smart cars. Aiming at the issues that conventional vehicle detection can be easily restricted by environmental conditions and cannot have accuracy and real-time performance, this article proposes a front vehicle detection algorithm for smart car based on improved SSD model. Single shot multibox detector (SSD) is one of the current mainstream object detection frameworks based on deep learning. This work first briefly introduces the SSD network model and analyzes and summarizes its problems and shortcomings in vehicle detection. Then, targeted improvements are performed to the SSD network model, including major advancements to the basic structure of the SSD model, the use of weighted mask in network training, and enhancement to the loss function. Finally, vehicle detection experiments are carried out on the basis of the KITTI vision benchmark suite and self-made vehicle dataset to observe the algorithm performance in different complicated environments and weather conditions. The test results based on the KITTI dataset show that the mAP value reaches 92.18%, and the average processing time per frame is 15 ms. Compared with the existing deep learning-based detection methods, the proposed algorithm can obtain accuracy and real-time performance simultaneously. Meanwhile, the algorithm has excellent robustness and environmental adaptability for complicated traffic environments and anti-jamming capabilities for bad weather conditions. These factors are of great significance to ensure the accurate and efficient operation of smart cars in real traffic scenarios and are beneficial to vastly reduce the incidence of traffic accidents and fully protect people’s lives and property.

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