scholarly journals GPU Accelerated Number Plate Localization in Crowded Situation

2015 ◽  
Vol 1 (3) ◽  
pp. 150 ◽  
Author(s):  
Adhi Prahara ◽  
Andri Pranolo ◽  
Rafał Dreżewski
Keyword(s):  
Real Time ◽  
Edge Density ◽  
Support Vector ◽  
Potential Region ◽  
Multi Scale ◽  
Balance Accuracy ◽  
Total Process ◽  
Real Time Detection ◽  
Scale Detection ◽  
Number Plate Localization

Number Plate Localization (NPL) has been widely used as part of Automatic Number Plate Recognition (ANPR) system. NPL method determines the accuracy of ANPR system. Although it is a mature research, the challenge stills persist especially in crowded situation where many vehicles present. Therefore, a method is proposed to localize number plate in crowded situation. The proposed NPL method uses vertical edge density to extract potential region of number plate then detect the number plate using combination of Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM). The method employs GPU to deal with multiple number plate detection, to handle multi-scale detection window, and to perform real time detection. The test result shows good results, 0.9883 value of AUC (Area Under Curve), and 0.9362 of BAC (Balance Accuracy). Moreover, potential real time detection is foreseen because total process is executed in less than 50 ms. Errors are mainly caused by background that contain letters, non-standard number plate and highly covered number plate

scholarly journals Support Vector Machine Algorithm for Real-Time Detection of VF Signals

2011 ◽  
Vol 8 ◽  
pp. 602-608
Author(s):  
Chunyun Zhang ◽  
Jie Zhao ◽  
Fei Li ◽  
Huilin Jia ◽  
Jie Tian
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Real Time Detection

scholarly journals Improved Pedestrian Detection Based on Extended Histogram of Oriented Gradients

2013 ◽  
Vol 347-350 ◽  
pp. 3815-3820
Author(s):  
Li Hong Zhang ◽  
Lin Li
Keyword(s):  
Detection System ◽  
Pedestrian Detection ◽  
Human Detection ◽  
Support Vector ◽  
Detection Accuracy ◽  
Histogram Of Oriented Gradients ◽  
Multi Scale ◽  
Suppression Method ◽  
Overlap Ratio ◽  
Scale Detection

In order to further improve pedestrian detection accuracy and avoid the disadvantage of original histogram of oriented gradients (HOG), differential template, overlap ratio and normalization method and so on are improved when HOG features are extracted, then more gradient information are extracted and feature description operators can be obtained which describe human detail features better in lager image regions or detection windows. Considering speed, we select support vector machine (SVM) using linear function kernel as a classifier. Multi-scale detection technique and non maxima suppression method are employed for precisely locating the pedestrians in the image. Experiments show that the human detection system improves detection accuracy and still maintains a relatively satisfactory speed.

scholarly journals DB-YOLO: A Duplicate Bilateral YOLO Network for Multi-Scale Ship Detection in SAR Images

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8146
Author(s):  
Haozhen Zhu ◽  
Yao Xie ◽  
Huihui Huang ◽  
Chen Jing ◽  
Yingjiao Rong ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Information ◽  
Detection Methods ◽  
Small Scale ◽  
Sar Images ◽  
Ship Detection ◽  
Multi Scale ◽  
Real Time Detection ◽  
Bounding Boxes ◽  
Improving Accuracy

With the wide application of convolutional neural networks (CNNs), a variety of ship detection methods based on CNNs in synthetic aperture radar (SAR) images were proposed, but there are still two main challenges: (1) Ship detection requires high real-time performance, and a certain detection speed should be ensured while improving accuracy; (2) The diversity of ships in SAR images requires more powerful multi-scale detectors. To address these issues, a SAR ship detector called Duplicate Bilateral YOLO (DB-YOLO) is proposed in this paper, which is composed of a Feature Extraction Network (FEN), Duplicate Bilateral Feature Pyramid Network (DB-FPN) and Detection Network (DN). Firstly, a single-stage network is used to meet the need of real-time detection, and the cross stage partial (CSP) block is used to reduce the redundant parameters. Secondly, DB-FPN is designed to enhance the fusion of semantic and spatial information. In view of the ships in SAR image are mainly distributed with small-scale targets, the distribution of parameters and computation values between FEN and DB-FPN in different feature layers is redistributed to solve the multi-scale detection. Finally, the bounding boxes and confidence scores are given through the detection head of YOLO. In order to evaluate the effectiveness and robustness of DB-YOLO, comparative experiments with the other six state-of-the-art methods (Faster R-CNN, Cascade R-CNN, Libra R-CNN, FCOS, CenterNet and YOLOv5s) on two SAR ship datasets, i.e., SSDD and HRSID, are performed. The experimental results show that the AP50 of DB-YOLO reaches 97.8% on SSDD and 94.4% on HRSID, respectively. DB-YOLO meets the requirement of real-time detection (48.1 FPS) and is superior to other methods in the experiments.

Real-Time Detection of Drowsiness Related Lane Departures Using Steering Wheel Angle

2012 ◽  
Author(s):  
Anthony D. McDonald ◽  
Chris Schwarz ◽  
John D. Lee ◽  
Timothy L. Brown
Keyword(s):  
Real Time ◽  
Steering Wheel ◽  
Real Time Detection
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