scholarly journals SPDNet: A Real-Time Passenger Detection Method Based on Attention Mechanism in Subway Station Scenes

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jun Yang ◽  
Ying Zheng ◽  
KunPing Yan ◽  
HongJiang Liu ◽  
Kun Jin ◽  
...  
Keyword(s):  
Real Time ◽  
Detection Method ◽  
Attention Mechanism ◽  
Subway Station ◽  
Detection Accuracy ◽  
Light Conditions ◽  
Surveillance Video ◽  
Surveillance Camera ◽  
Subway Stations ◽  
Beijing Subway

In order to implement real-time detection of passengers in subway stations, this paper proposes the SPDNet based on YOLOv4. Aiming at the low detection accuracy of passengers in the subway station due to uneven light conditions, we introduce the attention mechanism CBAM to recalibrate the extracted features and improve the robustness of the network. For the crowded areas in the subway station, we use the K-means++ algorithm to generate anchors that are more consistent with the passenger aspect ratio based on the dataset KITTI, which mitigates the missing caused by the incorrect suppression of true positive boxes by the Nonmaximum Suppression algorithm. We train and test our SPDNet on the KITTI dataset and prove the superiority of our method. Then, we carry out transfer learning based on the subway surveillance video dataset collected by ourselves to make it conform to the distorted passenger targets under the angle of the surveillance camera. Finally, we apply our network in a Beijing subway station and achieve satisfactory results.

scholarly journals Structural Damage Detection Based on Real-Time Vibration Signal and Convolutional Neural Network

2020 ◽  
Vol 10 (14) ◽  
pp. 4720 ◽  
Author(s):  
Zhiqiang Teng ◽  
Shuai Teng ◽  
Jiqiao Zhang ◽  
Gongfa Chen ◽  
Fangsen Cui
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Structural State ◽  
Detection Method ◽  
Steel Frame ◽  
Engineering Practice ◽  
Detection Accuracy ◽  
Vibration Signals

The traditional methods of structural health monitoring (SHM) have obvious disadvantages such as being time-consuming, laborious and non-synchronizing, and so on. This paper presents a novel and efficient approach to detect structural damages from real-time vibration signals via a convolutional neural network (CNN). As vibration signals (acceleration) reflect the structural response to the changes of the structural state, hence, a CNN, as a classifier, can map vibration signals to the structural state and detect structural damages. As it is difficult to obtain enough damage samples in practical engineering, finite element analysis (FEA) provides an alternative solution to this problem. In this paper, training samples for the CNN are obtained using FEA of a steel frame, and the effectiveness of the proposed detection method is evaluated by inputting the experimental data into the CNN. The results indicate that, the detection accuracy of the CNN trained using FEA data reaches 94% for damages introduced in the numerical model and 90% for damages in the real steel frame. It is demonstrated that the CNN has an ideal detection effect for both single damage and multiple damages. The combination of FEA and experimental data provides enough training and testing samples for the CNN, which improves the practicability of the CNN-based detection method in engineering practice.

scholarly journals A Real-Time Detection Method for Abnormal Data of Internet of Things Sensors Based on Mobile Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xuguang Liu
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Data Processing ◽  
Real Time ◽  
Detection Method ◽  
Edge Computing ◽  
Sensor Data ◽  
Detection Accuracy ◽  
Spatiotemporal Correlation ◽  
Real Time Detection

Aiming at the anomaly detection problem in sensor data, traditional algorithms usually only focus on the continuity of single-source data and ignore the spatiotemporal correlation between multisource data, which reduces detection accuracy to a certain extent. Besides, due to the rapid growth of sensor data, centralized cloud computing platforms cannot meet the real-time detection needs of large-scale abnormal data. In order to solve this problem, a real-time detection method for abnormal data of IoT sensors based on edge computing is proposed. Firstly, sensor data is represented as time series; K-nearest neighbor (KNN) algorithm is further used to detect outliers and isolated groups of the data stream in time series. Secondly, an improved DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm is proposed by considering spatiotemporal correlation between multisource data. It can be set according to sample characteristics in the window and overcomes the slow convergence problem using global parameters and large samples, then makes full use of data correlation to complete anomaly detection. Moreover, this paper proposes a distributed anomaly detection model for sensor data based on edge computing. It performs data processing on computing resources close to the data source as much as possible, which improves the overall efficiency of data processing. Finally, simulation results show that the proposed method has higher computational efficiency and detection accuracy than traditional methods and has certain feasibility.

Safety Assessment of Subway Station Operation Based on Grey Clustering Method

2013 ◽  
Vol 748 ◽  
pp. 1212-1216
Author(s):  
Xue Mei Xiao ◽  
Yan Hui Wang ◽  
Li Min Jia
Keyword(s):  
Safety Assessment ◽  
Evaluation Method ◽  
Safety Evaluation ◽  
Evaluation Index ◽  
Subway Station ◽  
Clustering Method ◽  
Weighting Method ◽  
Grey Clustering ◽  
Subway Stations ◽  
Beijing Subway

The safety evaluation index of subway station operation is established from the passengers, equipments and facilities ability, emergency ability and other aspects. Specific to the limitation the traditional entropy weighting method, the improved vertical and horizontal weighting method is puts forward, and safety evaluation method of the subway station operation based on grey clustering method is proposed. Finally, taking the Beijing subway stations in China as an example, the rationality and reliability of this method is verified.

scholarly journals SSD7-FFAM: A Real-Time Object Detection Network Friendly to Embedded Devices from Scratch

2021 ◽  
Vol 11 (3) ◽  
pp. 1096
Author(s):  
Qing Li ◽  
Yingcheng Lin ◽  
Wei He
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Large Scale ◽  
Feature Fusion ◽  
Contextual Information ◽  
Attention Mechanism ◽  
Detection Accuracy ◽  
Single Shot ◽  
Feature Maps ◽  
Embedded Devices

The high requirements for computing and memory are the biggest challenges in deploying existing object detection networks to embedded devices. Living lightweight object detectors directly use lightweight neural network architectures such as MobileNet or ShuffleNet pre-trained on large-scale classification datasets, which results in poor network structure flexibility and is not suitable for some specific scenarios. In this paper, we propose a lightweight object detection network Single-Shot MultiBox Detector (SSD)7-Feature Fusion and Attention Mechanism (FFAM), which saves storage space and reduces the amount of calculation by reducing the number of convolutional layers. We offer a novel Feature Fusion and Attention Mechanism (FFAM) method to improve detection accuracy. Firstly, the FFAM method fuses high-level semantic information-rich feature maps with low-level feature maps to improve small objects’ detection accuracy. The lightweight attention mechanism cascaded by channels and spatial attention modules is employed to enhance the target’s contextual information and guide the network to focus on its easy-to-recognize features. The SSD7-FFAM achieves 83.7% mean Average Precision (mAP), 1.66 MB parameters, and 0.033 s average running time on the NWPU VHR-10 dataset. The results indicate that the proposed SSD7-FFAM is more suitable for deployment to embedded devices for real-time object detection.

scholarly journals Flower End-to-End Detection Based on YOLOv4 Using a Mobile Device

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Zhibin Cheng ◽  
Fuquan Zhang
Keyword(s):  
Computer Vision ◽  
Loss Function ◽  
Mobile Device ◽  
Detection Method ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Detection Accuracy ◽  
Detection Algorithms ◽  
End To End ◽  
Detection Speed

In this paper, a novel flower detection application anchor-based method is proposed, which is combined with an attention mechanism to detect the flowers in a smart garden in AIoT more accurately and fast. While many researchers have paid much attention to the flower classification in existing studies, the issue of flower detection has been largely overlooked. The problem we have outlined deals largely with the study of a new design and application of flower detection. Firstly, a new end-to-end flower detection anchor-based method is inserted into the architecture of the network to make it more precious and fast and the loss function and attention mechanism are introduced into our model to suppress unimportant features. Secondly, our flower detection algorithms can be integrated into the mobile device. It is revealed that our flower detection method is very considerable through a series of investigations carried out. The detection accuracy of our method is similar to that of the state-of-the-art, and the detection speed is faster at the same time. It makes a major contribution to flower detection in computer vision.

scholarly journals Design and Application of Risk Early Warning System for Subway Station Construction Based on Building Information Modeling Real-Time Model

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Qianlong Tang ◽  
Mingfeng Lei ◽  
Binbin Zhu ◽  
Limin Peng ◽  
Weimin Wu ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Building Information Modeling ◽  
Warning System ◽  
Information Modeling ◽  
Subway Station ◽  
Grey Prediction ◽  
Building Information ◽  
Subway Stations

The problems faced by subway stations in the construction process are more complex than those by overground buildings. Therefore, the construction risk for such structures is highly unpredictable and the risk management is difficult. Building information modeling (BIM) technology has strong visualization, simulation, and integration characteristics that make it conducive to the development of a risk early warning system for underground engineering. According to the functional requirements of risk early warning for subway stations, a risk early warning system based on a BIM real-time construction model is designed in this study for a subway station construction. The operation process of the risk early warning system is established through the grey prediction method to propose the operation method of the early warning system. The early warning system is applied to the Xiangjiang New Town Station of Changsha Metro Line 4 in China to verify its feasibility.

scholarly journals Real-Time Jellyfish Classification and Detection Based on Improved YOLOv3 Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8160
Author(s):  
Meijing Gao ◽  
Yang Bai ◽  
Zhilong Li ◽  
Shiyu Li ◽  
Bozhi Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Detection Method ◽  
Histogram Equalization ◽  
Detection Accuracy ◽  
Data Set ◽  
Marine Fishery ◽  
Multi Scale ◽  
Underwater Image ◽  
Neural Network Theory

In recent years, jellyfish outbreaks have frequently occurred in offshore areas worldwide, posing a significant threat to the marine fishery, tourism, coastal industry, and personal safety. Effective monitoring of jellyfish is a vital method to solve the above problems. However, the optical detection method for jellyfish is still in the primary stage. Therefore, this paper studies a jellyfish detection method based on convolution neural network theory and digital image processing technology. This paper studies the underwater image preprocessing algorithm because the quality of underwater images directly affects the detection results. The results show that the image quality is better after applying the three algorithms namely prior defogging, adaptive histogram equalization, and multi-scale retinal enhancement, which is more conducive to detection. We establish a data set containing seven species of jellyfishes and fish. A total of 2141 images are included in the data set. The YOLOv3 algorithm is used to detect jellyfish, and its feature extraction network Darknet53 is optimized to ensure it is conducted in real-time. In addition, we introduce label smoothing and cosine annealing learning rate methods during the training process. The experimental results show that the improved algorithms improve the detection accuracy of jellyfish on the premise of ensuring the detection speed. This paper lays a foundation for the construction of an underwater jellyfish optical imaging real-time monitoring system.

scholarly journals Human Activity Recognition and Location Based on Temporal Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Hongjin Ding ◽  
Faming Gong ◽  
Wenjuan Gong ◽  
Xiangbing Yuan ◽  
Yuhui Ma
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Human Activity ◽  
Temporal Analysis ◽  
Human Activity Recognition ◽  
Detection Accuracy ◽  
Production Plant ◽  
Surveillance Video ◽  
Multiple Sensors ◽  
Time Performance

Current methods of human activity recognition face many challenges, such as the need for multiple sensors, poor implementation, unreliable real-time performance, and lack of temporal location. In this research, we developed a method for recognizing and locating human activities based on temporal action recognition. For this work, we used a multilayer convolutional neural network (CNN) to extract features. In addition, we used refined actionness grouping to generate precise region proposals. Then, we classified the candidate regions by employing an activity classifier based on a structured segmented network and a cascade design for end-to-end training. Compared with previous methods of action classification, the proposed method adds the time boundary and effectively improves the detection accuracy. To test this method empirically, we conducted experiments utilizing surveillance video of an offshore oil production plant. Three activities were recognized and located in the untrimmed long video: standing, walking, and falling. The accuracy of the results proved the effectiveness and real-time performance of the proposed method, demonstrating that this approach has great potential for practical application.

scholarly journals Research on Obstacle Detection Method of Urban Rail Transit Based on Multisensor Technology

2021 ◽  
Vol 1 (1) ◽  
pp. 61-67
Author(s):  
Tianwen Xiao ◽  
Yongneng Xu ◽  
Huimin Yu
Keyword(s):  
Real Time ◽  
Detection Method ◽  
Obstacle Detection ◽  
Response Mode ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Detection Accuracy ◽  
Position Information ◽  
Urban Rail ◽  
Train Operation

With the rapid development of urban rail transit, passenger traffic is increasing, and obstacle violations are more frequent, and the safety of train operation under high-density traffic conditions is becoming more and more thought-provoking. In order to monitor the train operating environment in real time, this paper first adopts multi-sensing technology based on machine vision and lidar, which is used to collect video images and ranging data of the track area in real time, and then it performs image preprocessing and division of regions of interest on the collected video. Then, the obstacles in the region of interest are detected to obtain the geometric characteristics and position information of the obstacles. Finally, according to the danger level of the obstacles, determine the degree of impact on train operation , the automatic response mode and manual response mode of the signal system are used to transmit the detection results to the corresponding train to control train operation. Through simulation analysis and experimental verification, the detection accuracy and control performance of the detection method are confirmed, which provides safety guarantee for the train operation.

scholarly journals Vehicle Detection Using Deep Learning Technique in Tunnel Road Environments

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2012
Author(s):  
JongBae Kim
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Detection Method ◽  
Vehicle Detection ◽  
Ground Truth ◽  
Noise Removal ◽  
Detection Accuracy ◽  
Vehicle Exhaust ◽  
Learning Techniques ◽  
Real Time Detection

This paper proposes a real-time detection method for a car driving ahead in real time on a tunnel road. Unlike the general road environment, the tunnel environment is irregular and has significantly lower illumination, including tunnel lighting and light reflected from driving vehicles. The environmental restrictions are large owing to pollution by vehicle exhaust gas. In the proposed method, a real-time detection method is used for vehicles in tunnel images learned in advance using deep learning techniques. To detect the vehicle region in the tunnel environment, brightness smoothing and noise removal processes are carried out. The vehicle region is learned after generating a learning image using the ground-truth method. The YOLO v2 model, with an optimal performance compared to the performances of deep learning algorithms, is applied. The training parameters are refined through experiments. The vehicle detection rate is approximately 87%, while the detection accuracy is approximately 94% for the proposed method applied to various tunnel road environments.

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