scholarly journals NRA-Net—Neg-Region Attention Network for Salient Object Detection with Gaze Tracking

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1753
Author(s):  
Hoijun Kim ◽  
Soonchul Kwon ◽  
Seunghyun Lee
Keyword(s):  
Deep Learning ◽  
Detection Method ◽  
Activation Function ◽  
Salient Object Detection ◽  
Line Of Sight ◽  
Single Image ◽  
Gaze Tracking ◽  
Learning Techniques ◽  
Object Area ◽  
Background Area

In this paper, we propose a detection method for salient objects whose eyes are focused on gaze tracking; this method does not require a device in a single image. A network was constructed using Neg-Region Attention (NRA), which predicts objects with a concentrated line of sight using deep learning techniques. The existing deep learning-based method has an autoencoder structure, which causes feature loss during the encoding process of compressing and extracting features from the image and the decoding process of expanding and restoring. As a result, a feature loss occurs in the area of the object from the detection results, or another area is detected as an object. The proposed method, that is, NRA, can be used for reducing feature loss and emphasizing object areas with encoders. After separating positive and negative regions using the exponential linear unit activation function, converted attention was performed for each region. The attention method provided without using the backbone network emphasized the object area and suppressed the background area. In the experimental results, the proposed method showed higher detection results than the conventional methods.

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 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.

Saliency detection in deep learning era: trends of development

2019 ◽  
pp. 10-36 ◽  
Author(s):  
M. N. Favorskaya ◽  
L. C. Jain
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Event Detection ◽  
Visual Analysis ◽  
Saliency Detection ◽  
Salient Object Detection ◽  
Public Image ◽  
Detection Methods ◽  
Salient Object ◽  
Salient Event

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.

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.

Deep Learning Techniques for Koala Activity Detection

2018 ◽  
Author(s):  
Ivan Himawan ◽  
Michael Towsey ◽  
Bradley Law ◽  
Paul Roe
Keyword(s):  
Deep Learning ◽  
Activity Detection ◽  
Learning Techniques
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