scholarly journals Correction: Ye, T.; et al. Automatic Railway Traffic Object Detection System Using Feature Fusion Refine Neural Network under Shunting Mode. Sensors 2018, 18, 1916

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3044
Author(s):  
Tao Ye ◽  
Baocheng Wang ◽  
Ping Song ◽  
Juan Li
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Feature Fusion ◽  
Detection System ◽  
Railway Traffic

The authors wish to make the following corrections to this paper [...]

scholarly journals Automatic Railway Traffic Object Detection System Using Feature Fusion Refine Neural Network under Shunting Mode

Sensors ◽  
2018 ◽  
Vol 18 (6) ◽  
pp. 1916 ◽  
Author(s):  
Tao Ye ◽  
Baocheng Wang ◽  
Ping Song ◽  
Juan Li
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Feature Fusion ◽  
Detection System ◽  
Railway Traffic

scholarly journals Developing an On-Road Object Detection System Using Monovision and Radar Fusion

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 116
Author(s):  
Ya-Wen Hsu ◽  
Yi-Horng Lai ◽  
Kai-Quan Zhong ◽  
Tang-Kai Yin ◽  
Jau-Woei Perng
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Spatial Clustering ◽  
Detection System ◽  
Warning System ◽  
Recognition System ◽  
Target Object ◽  
Fusion Algorithm ◽  
Detection Rates ◽  
Distance Information

In this study, a millimeter-wave (MMW) radar and an onboard camera are used to develop a sensor fusion algorithm for a forward collision warning system. This study proposed integrating an MMW radar and camera to compensate for the deficiencies caused by relying on a single sensor and to improve frontal object detection rates. Density-based spatial clustering of applications with noise and particle filter algorithms are used in the radar-based object detection system to remove non-object noise and track the target object. Meanwhile, the two-stage vision recognition system can detect and recognize the objects in front of a vehicle. The detected objects include pedestrians, motorcycles, and cars. The spatial alignment uses a radial basis function neural network to learn the conversion relationship between the distance information of the MMW radar and the coordinate information in the image. Then a neural network is utilized for object matching. The sensor with a higher confidence index is selected as the system output. Finally, three kinds of scenario conditions (daytime, nighttime, and rainy-day) were designed to test the performance of the proposed method. The detection rates and the false alarm rates of proposed system were approximately 90.5% and 0.6%, respectively.

scholarly journals Deep Learning Object Detector Using a Combination of Convolutional Neural Network (CNN) Architecture (MiniVGGNet) and Classic Object Detection Algorithm

2020 ◽  
Vol 28 (S2) ◽  
Author(s):  
Asmida Ismail ◽  
Siti Anom Ahmad ◽  
Azura Che Soh ◽  
Mohd Khair Hassan ◽  
Hazreen Haizi Harith
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Object Classification ◽  
Detection Algorithm ◽  
Learning Object ◽  
Sliding Windows ◽  
Detection Algorithms

The object detection system is a computer technology related to image processing and computer vision that detects instances of semantic objects of a certain class in digital images and videos. The system consists of two main processes, which are classification and detection. Once an object instance has been classified and detected, it is possible to obtain further information, including recognizes the specific instance, track the object over an image sequence and extract further information about the object and the scene. This paper presented an analysis performance of deep learning object detector by combining a deep learning Convolutional Neural Network (CNN) for object classification and applies classic object detection algorithms to devise our own deep learning object detector. MiniVGGNet is an architecture network used to train an object classification, and the data used for this purpose was collected from specific indoor environment building. For object detection, sliding windows and image pyramids were used to localize and detect objects at different locations, and non-maxima suppression (NMS) was used to obtain the final bounding box to localize the object location. Based on the experiment result, the percentage of classification accuracy of the network is 80% to 90% and the time for the system to detect the object is less than 15sec/frame. Experimental results show that there are reasonable and efficient to combine classic object detection method with a deep learning classification approach. The performance of this method can work in some specific use cases and effectively solving the problem of the inaccurate classification and detection of typical features.

MFC-Net : Multi-feature fusion cross neural network for salient object detection

2021 ◽  
pp. 104243
Author(s):  
Zhenyu Wang ◽  
Yunzhou Zhang ◽  
Yan Liu ◽  
Shichang Liu ◽  
Sonya Coleman ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Feature Fusion ◽  
Salient Object Detection ◽  
Salient Object

scholarly journals Intelligent Image Saliency Detection Method Based on Convolution Neural Network Combining Global and Local Information

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Songshang Zou ◽  
Wenshu Chen ◽  
Hao Chen
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Feature Fusion ◽  
Essential Feature ◽  
Saliency Detection ◽  
Experimental Result ◽  
Image Saliency ◽  
Detection Technology ◽  
Global And Local ◽  
Saliency Object Detection

Image saliency object detection can rapidly extract useful information from image scenes and further analyze it. At present, the traditional saliency target detection technology still has the edge of outstanding target that cannot be well preserved. Convolutional neural network (CNN) can extract highly general deep features from the images and effectively express the essential feature information of the images. This paper designs a model which applies CNN in deep saliency object detection tasks. It can efficiently optimize the edges of foreground objects and realize highly efficient image saliency detection through multilayer continuous feature extraction, refinement of layered boundary, and initial saliency feature fusion. The experimental result shows that the proposed method can achieve more robust saliency detection to adjust itself to complex background environment.

scholarly journals Brand Detection System using Deep Learning

2020 ◽  
Vol 9 (9) ◽  
pp. 497-500
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Visual Representation ◽  
Cosmetic Products ◽  
Medical Object

In this paper a method of recognizing logos of the brand of cosmetic products using deep learning. There are several of hoax product which easily copies the famous brand’s logo and deteriorates the company’s image. The machine learning has proved to be useful in various of the fields like medical, object detection, vehicle logo recognitions. But till now very few of the works have been performed in cosmetic field. This field is covered using the model sequential convolutional neural network using Tensorflow and Keras. For the visual representation of the result Tensorboard is used. Work have been started with two of the brands-Lakme and L’Oreal. Depending upon the success of this technique, further brands for logo may be added for recognition. The accuracy of approximately 80% was obtained using this technique.

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