Comparison of two vehicle detection methods based on the oriented gradients histogram and the deep neural network

2019 ◽  
Author(s):  
Fadwa Benjelloun ◽  
Khalid Abbad ◽  
My Abdelouahed Sabri ◽  
Abdellah Aarab ◽  
Ali Yahyaouy
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Vehicle Detection ◽  
Detection Methods

Vehicle Detection in Thermal Images Using Deep Neural Network

2018 ◽  
Author(s):  
Chin-Wei Chang ◽  
Kathiravan Srinivasan ◽  
Yung-Yao Chen ◽  
Wen-Huang Cheng ◽  
Kai-Lung Hua
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Vehicle Detection ◽  
Thermal Images

scholarly journals Small UAV Target Detection Model Based on Deep Neural Network

2018 ◽  
Vol 36 (2) ◽  
pp. 258-263 ◽  
Author(s):  
Jingyu Wang ◽  
Xianyu Wang ◽  
Ke Zhang ◽  
Yilun Cai ◽  
Yue Liu
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Engineering Application ◽  
Recognition Task ◽  
Detection Methods ◽  
Detection Model ◽  
Small Uav ◽  
Aerial Vehicle ◽  
Visual Characteristics ◽  
Uav Image

Unmanned aerial vehicle (UAV) has relatively small size and weak visual characteristics. The recognition accuracy of traditional object detection methods can decrease sharply when complex background and distraction objects exist. In this paper, we proposed a novel deep neural network (DNN) model for small UAV target recognition task. Based on the visual characteristics of surveillance image and UAV target, a multi-channel DNN is designed. Training and optimization of the DNN are completed with self-constructed UAV image database. Simulation results show that the proposed DNN model can achieve good results in recognizing the variable-scale UAV target and have compatible performance in distinguishing the interference and that the proposed model is robust and has a great potential prospect for engineering application.

scholarly journals Efficient Deep Network Architecture for Vision-Based Vehicle Detection Keyvan Kasiri,

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Keyvan Kasiri ◽  
Mohammad Javad Shafiee ◽  
Francis Li ◽  
Alexander Wong ◽  
Justin Eichel
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Vehicle Detection ◽  
Transportation Systems ◽  
Surveillance Systems ◽  
Performance Accuracy ◽  
Sparse Set

With the progress in intelligent transportation systems in smartcities, vision-based vehicle detection is becoming an important issuein the vision-based surveillance systems. With the advent ofthe big data era, deep learning methods have been increasinglyemployed in the detection, classification, and recognition applicationsdue to their performance accuracy, however, there are stillmajor concerns regarding deployment of such methods in embeddedapplications. This paper offers an efficient process leveragingthe idea of evolutionary deep intelligence on a state-of-the-art deepneural network. Using this approach, the deep neural network isevolved towards a highly sparse set of synaptic weights and clusters.Experimental results for the task of vehicle detection demonstratethat the evolved deep neural network can achieve a substantialimprovement in architecture efficiency adapting for GPUacceleratedapplications without significant sacrifices in detectionaccuracy. The architectural efficiency of ~4X-fold and ~2X-folddecrease is obtained in synaptic weights and clusters, respectively,while the accuracy of 92.8% (drop of less than 4% compared to theoriginal network model) is achieved. Detection results and networkefficiency for the vehicular application are promising, and opensthe door to a wider range of applications in deep learning.

Effective human detection via multi-model classification and adaptive late fusion

2018 ◽  
Vol 16 (02) ◽  
pp. 1840012
Author(s):  
Chao Zhu ◽  
Xu-Cheng Yin
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Human Detection ◽  
Detection Methods ◽  
Late Fusion ◽  
Model Classification ◽  
Detection Approach ◽  
Feature Based ◽  
Multiple State

Human detection serves as an important basis to achieve certain video surveillance-oriented biometrics such as gait, face and actions since the first step is to find and locate human targets in surveillance scenes. In the literature, channel feature-based methods and deep neural network-based methods are two most popular kinds of human detection approaches, with their own advantages. However, there is not much effort on the study of their combination to take full advantage of these two kinds of approaches. Therefore in this paper, we propose an effective human detection approach by combining multiple state-of-the-art deep neural network-based and channel feature-based methods with an adaptive late fusion strategy. The key idea of our approach is to explore complementary information of different state-of-the-art detection methods and to find an appropriate way to combine their strong points for better performance. The proposed approach is evaluated on several standard human detection benchmarks, and shows its effectiveness by achieving superior performances to the other state-of-the-art methods on most evaluation settings.

scholarly journals Real-Time Detection for Wheat Head Applying Deep Neural Network

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 191
Author(s):  
Bo Gong ◽  
Daji Ergu ◽  
Ying Cai ◽  
Bo Ma
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Detection Methods ◽  
Convolutional Network ◽  
Head Detection ◽  
Real Time Detection ◽  
Low Efficiency ◽  
Basic Network

Wheat head detection can estimate various wheat traits, such as density, health, and the presence of wheat head. However, traditional detection methods have a huge array of problems, including low efficiency, strong subjectivity, and poor accuracy. In this paper, a method of wheat-head detection based on a deep neural network is proposed to enhance the speed and accuracy of detection. The YOLOv4 is taken as the basic network. The backbone part in the basic network is enhanced by adding dual spatial pyramid pooling (SPP) networks to improve the ability of feature learning and increase the receptive field of the convolutional network. Multilevel features are obtained by a multipath neck part using a top-down to bottom-up strategy. Finally, YOLOv3′s head structures are used to predict the boxes of wheat heads. For training images, some data augmentation technologies are used. The experimental results demonstrate that the proposed method has a significant advantage in accuracy and speed. The mean average precision of our method is 94.5%, and the detection speed is 71 FPS that can achieve the effect of real-time detection.

scholarly journals FGSC: Fuzzy Guided Scale Choice SSD Model for Edge AI Design on Real-Time Vehicle Detection and Class Counting

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7399
Author(s):  
Ming-Hwa Sheu ◽  
S M Salahuddin Morsalin ◽  
Jia-Xiang Zheng ◽  
Shih-Chang Hsia ◽  
Cheng-Jian Lin ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Vehicle Detection ◽  
Traffic Monitoring ◽  
Sigmoid Function ◽  
Real Time Processing ◽  
Scale Choice ◽  
Training Stage

The aim of this paper is to distinguish the vehicle detection and count the class number in each classification from the inputs. We proposed the use of Fuzzy Guided Scale Choice (FGSC)-based SSD deep neural network architecture for vehicle detection and class counting with parameter optimization. The 'FGSC' blocks are integrated into the convolutional layers of the model, which emphasize essential features while ignoring less important ones that are not significant for the operation. We created the passing detection lines and class counting windows and connected them with the proposed FGSC-SSD deep neural network model. The 'FGSC' blocks in the convolution layer emphasize essential features and find out unnecessary features by using the scale choice method at the training stage and eliminate that significant speedup of the model. In addition, FGSC blocks avoided many unusable parameters in the saturation interval and improved the performance efficiency. In addition, the Fuzzy Sigmoid Function (FSF) increases the activation interval through fuzzy logic. While performing operations, the FGSC-SSD model reduces the computational complexity of convolutional layers and their parameters. As a result, the model tested Frames Per Second (FPS) on edge artificial intelligence (AI) and reached a real-time processing speed of 38.4 and an accuracy rate of more than 94%. Therefore, this work might be considered an improvement to the traffic monitoring approach by using edge AI applications.

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