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.

scholarly journals Real-Time Vehicle Detection Algorithm Based on Vision and Lidar Point Cloud Fusion

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Hai Wang ◽  
Xinyu Lou ◽  
Yingfeng Cai ◽  
Yicheng Li ◽  
Long Chen
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Point Cloud ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Depth Information ◽  
Detection Accuracy ◽  
Classification Rate

Vehicle detection is one of the most important environment perception tasks for autonomous vehicles. The traditional vision-based vehicle detection methods are not accurate enough especially for small and occluded targets, while the light detection and ranging- (lidar-) based methods are good in detecting obstacles but they are time-consuming and have a low classification rate for different target types. Focusing on these shortcomings to make the full use of the advantages of the depth information of lidar and the obstacle classification ability of vision, this work proposes a real-time vehicle detection algorithm which fuses vision and lidar point cloud information. Firstly, the obstacles are detected by the grid projection method using the lidar point cloud information. Then, the obstacles are mapped to the image to get several separated regions of interest (ROIs). After that, the ROIs are expanded based on the dynamic threshold and merged to generate the final ROI. Finally, a deep learning method named You Only Look Once (YOLO) is applied on the ROI to detect vehicles. The experimental results on the KITTI dataset demonstrate that the proposed algorithm has high detection accuracy and good real-time performance. Compared with the detection method based only on the YOLO deep learning, the mean average precision (mAP) is increased by 17%.

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.

Real-Time Detection Method of Persistent Objects in Radar Imagery with Deep Learning

2020 ◽  
Author(s):  
Rosa Gonzales Martinez ◽  
Javier Machacuay Vera ◽  
Cesar Chinguel Arrese
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Detection Method ◽  
Radar Imagery ◽  
Real Time Detection

scholarly journals An Approach on Image Processing of Deep Learning Based on Improved SSD

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 495
Author(s):  
Liang Jin ◽  
Guodong Liu
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Remote Sensing Image ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Image Detection ◽  
Ship Detection ◽  
Real Time Detection

Compared with ordinary images, each of the remote sensing images contains many kinds of objects with large scale changes, providing more details. As a typical object of remote sensing image, ship detection has been playing an essential role in the field of remote sensing. With the rapid development of deep learning, remote sensing image detection method based on convolutional neural network (CNN) has occupied a key position. In remote sensing images, the objects of which small scale objects account for a large proportion are closely arranged. In addition, the convolution layer in CNN lacks ample context information, leading to low detection accuracy for remote sensing image detection. To improve detection accuracy and keep the speed of real-time detection, this paper proposed an efficient object detection algorithm for ship detection of remote sensing image based on improved SSD. Firstly, we add a feature fusion module to shallow feature layers to refine feature extraction ability of small object. Then, we add Squeeze-and-Excitation Network (SE) module to each feature layers, introducing attention mechanism to network. The experimental results based on Synthetic Aperture Radar ship detection dataset (SSDD) show that the mAP reaches 94.41%, and the average detection speed is 31FPS. Compared with SSD and other representative object detection algorithms, this improved algorithm has a better performance in detection accuracy and can realize real-time detection.

Real-time detection method of driver fatigue state based on deep learning of face video

2021 ◽  
Author(s):  
Zhe Cui ◽  
Hong-Mei Sun ◽  
Ruo-Nan Yin ◽  
Li Gao ◽  
Hai-Bin Sun ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Detection Method ◽  
Driver Fatigue ◽  
Real Time Detection

scholarly journals Deep Learning Techniques for the Real Time Detection of Covid19 and Pneumonia using Chest Radiographs

2021 ◽  
Author(s):  
Avnish Panwar ◽  
Rishika Yadav ◽  
Kishor Mishra ◽  
Siddharth Gupta
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Chest Radiographs ◽  
The Real ◽  
Learning Techniques ◽  
Real Time Detection

scholarly journals Object Detection Method Based on YOLOv3 using Deep Learning Networks

2019 ◽  
Vol 9 (1) ◽  
pp. 1414-1417
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Real World ◽  
Detection Method ◽  
Cross Entropy ◽  
Detection Methods ◽  
Learning Networks ◽  
Learning Techniques ◽  
Localization Errors

—Object Detection is being widely used in the industry right now. It is the method of detection and shaping real-world objects. Even though there exist many detection methods, the accuracy, rapidity, and efficiency of detection are not good enough. So, this paper demonstrates real-time detection using the YOLOv3 algorithm by deep learning techniques. It first makes expectations crosswise over 3 unique scales. The identification layer is utilized to make recognition at highlight maps of three distinct sizes, having strides 32, 16, 8 individually. This implies, with partner contribution of 416 x 416, we will in general form location on scales 13 x 13, 26 x 26 and 52x 52. Meanwhile, it also makes use of strategic relapse to anticipate the jumping box article score, the paired cross-entropy misfortune is utilized to foresee the classes that the bounding box may contain, the certainty is determined and afterward the forecast. It results in perform multi-label classification for objects detected in images, the average preciseness for tiny objects improved, it's higher than quicker RCNN. MAP increased significantly. As MAP increased localization errors decreased.

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

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