Blob Detection with the Determinant of the Hessian

Author(s):  
Xiaopeng Xu
Keyword(s):  
Author(s):  
Manjiri Bichkar ◽  
Suyasha Bobhate ◽  
Prof. Sonal Chaudhari

This paper presents an effective solution to detecting traffic signs on road by first classifying the traffic sign images us-ing Convolutional Neural Network (CNN) on the German Traffic Sign Recognition Benchmark (GTSRB)[1] and then detecting the images of Indian Traffic Signs using the Indian Dataset which will be used as testing dataset while building classification model. Therefore this system helps electric cars or self driving cars to recognise the traffic signs efficiently and correctly. The system involves two parts, detection of traffic signs from the environment and classification based on CNN thereby recognising the traffic sign. The classification involves building a CNN model of different filters of dimensions 3 × 3, 5 × 5, 9 × 9, 13 × 13, 15 × 15,19 × 19, 23 × 23, 25 × 25 and 31 ×31 from which the most efficient filter is chosen for further classifying the image detected. The detection involves detecting the traffic sign using YOLO v3-v4 and BLOB detection. Transfer Learning is used for using the trained model for detecting Indian traffic sign images.


2019 ◽  
Vol 12 (4) ◽  
pp. 1585-1626 ◽  
Author(s):  
Rafael Reisenhofer ◽  
Emily J. King
Keyword(s):  

Author(s):  
Krit Inthajak ◽  
Cattleya Duanggate ◽  
Bunyarit Uyyanonvara ◽  
Stanislav S. Makhanov ◽  
Sarah Barman

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