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Traffic Sign Classification based on Neural Network for Advance Driver Assistance System

100%

Hannan M. A. , Wali S. B. , Pin T. J. , Hussain A. , Samad S. A.

tom R. 90, nr 11

169--172

Traffic sign is utmost important information or rule in transportation. In order to ensure the transportation safety the automotive industry has developed Advance Driver Assistance System (ADAS). Among the ADAS system, development of TSDR is the most challenging to the researchers and developers due to unsatisfying performance. This paper deals with, automatic traffic sign classification and reduces the effect of illumination and variable lighting over the classification scheme by using neural network according to the traffic sign shape. There are three main phase of the classification scheme such as; pre-processing using image normalization, feature extraction using color information of 16-point pixel values and multilayer feed forward neural network for classification. An accuracy rate of 84.4% has been achieved by the proposed system. Overall processing time of 0.134s shows the system is a fast system and real-time application.

W artykule opisano metodę automatycznego rozpoznawania I klasyfikacji znaków drogowych z przenaczeniem do inteligentnych systemów wspomagania kierowcy ADAS. Do tego celu wykorzystano sieci neuronowe przeprowadzając normalizację obrazu, ekstrakcję cech i klasyfikację. Osiągnieto dokładność rozpoznawania rzędu 84% przy przeciętnym czasie rozpoznawania około 0.13 s.

Vision-based positioning of electric buses for assisted docking to charging stations

84%

Nowak T. , Nowicki M. R. , Skrzypczyński P.

tom Vol. 32, no. 4

583--599

We present a novel approach to vision-based localization of electric city buses for assisted docking to a charging station. The method assumes that the charging station is a known object, and employs a monocular camera system for positioning upon carefully selected point features detected on the charging station. While the pose is estimated using a geometric method and taking advantage of the known structure of the feature points, the detection of keypoints themselves and the initial recognition of the charging station are accomplished using neural network models. We propose two novel neural network architectures for the estimation of keypoints. Extensive experiments presented in the paper made it possible to select the MRHKN architecture as the one that outperforms state-of-the-art keypoint detectors in the task considered, and offers the best performance with respect to the estimated translation and rotation of the bus with a low-cost hardware setup and minimal passive markers on the charging station.