Real-time detection network for tiny traffic sign using multi-scale attention module

2021 ◽  
Author(s):  
TingTing Yang ◽  
Chao Tong
Keyword(s):  
Real Time ◽  
Traffic Sign ◽  
Multi Scale ◽  
Real Time Detection

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 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.

scholarly journals Detection of Small Size Traffic Signs Using Regressive Anchor Box Selection and DBL Layer Tweaking in YOLOv3

2021 ◽  
Vol 11 (23) ◽  
pp. 11555
Author(s):  
Yawar Rehman ◽  
Hafsa Amanullah ◽  
Dost Muhammad Saqib Bhatti ◽  
Waqas Tariq Toor ◽  
Muhammad Ahmad ◽  
...  
Keyword(s):  
Real Time ◽  
Detection Accuracy ◽  
Optimal Size ◽  
Image Size ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Sign Detection ◽  
Real Time Detection ◽  
Traffic Sign Detection

Traffic sign recognition is a key module of autonomous cars and driver assistance systems. Traffic sign detection accuracy and inference time are the two most important parameters. Current methods for traffic sign recognition are very accurate; however, they do not meet the requirement for real-time detection. While some are fast enough for real-time traffic sign detection, they fall short in accuracy. This paper proposes an accuracy improvement in the YOLOv3 network, which is a very fast detection framework. The proposed method contributes to the accurate detection of a small-sized traffic sign in terms of image size and helps to reduce false positives and miss rates. In addition, we propose an anchor frame selection algorithm that helps in achieving the optimal size and scale of the anchor frame. Therefore, the proposed method supports the detection of a small traffic sign with real-time detection. This ultimately helps to achieve an optimal balance between accuracy and inference time. The proposed network is evaluated on two publicly available datasets, namely the German Traffic Sign Detection Benchmark (GTSDB) and the Swedish Traffic Sign dataset (STS), and its performance showed that the proposed approach achieves a decent balance between mAP and inference time.

scholarly journals Real-Time Detection and Recognition of Road Traffic Signs using MSER and Random Forests

2018 ◽  
Vol 14 (03) ◽  
pp. 34 ◽  
Author(s):  
Xianyan Kuang ◽  
Wenbin Fu ◽  
Liu Yang
Keyword(s):  
Real Time ◽  
Random Forests ◽  
Road Traffic ◽  
Contrast Ratio ◽  
Region Of Interest ◽  
Traffic Sign ◽  
Data Set ◽  
Traffic Signs ◽  
Real Time Detection ◽  
Detection And Recognition

Real-time detection and recognition of road traffic signs plays an important role in advanced driving assistance system. Typically, the region of interest (ROI) method is effective in feature extraction but inefficient because it is sensitive to illumination changes. In this paper, we propose a maximally stable extremal regions (MSER) method with image enhancement to greatly improve ROI. Firstly, we employ gray world algorithm to process original images. And then potential areas of traffic signs are obtained through increasing the image contrast ratio and extracting the image-enhanced MSER. According to the characteristic variable and the geometry moment invariants, the geometric characteristics of traffic signs are extracted to obtain the ROIs. Finally, HSV-HOG-LBP feature is constructed and the random forests algorithm is used to identify the traffic signs. The experimental results show that our proposed method show strong robustness on illumination condition and rotation scale, and achieves a good performance by experiments with actual images and German traffic sign detection benchmark (GTSDB) data set.

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

Walking Motion Real-time Detection Based on Walking Stick Equipped with MPU and Raspberry Pi

2019 ◽  
Author(s):  
Junyi Wang ◽  
Mohamad Saada ◽  
Haibin Cai ◽  
Qinggang Meng
Keyword(s):  
Real Time ◽  
Raspberry Pi ◽  
Walking Motion ◽  
Real Time Detection ◽  
Walking Stick
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