Lane Detection Based on Block-wise Hough Based Features and ANN

Abstract

Effective and automated lane marking detection system is one of the important part of many intelligent transportation systems. Many researches of lane detection techniques have been conducted on academic and automobile industry and applied in intelligent transportation systems. However, It is still a challenging problem because of the various environmental conditions such as shadows, lane occlusion and illumination invariance that can decrease the lane detection performance. To deal with these issues, a reliable lane marking detection system that use the block-wise Hough based features is proposed. We propose the Region of interest extraction that divides the image into sub-image blocks to detect the lane line and block-wise Hough Transform feature extraction approach to extract the lane line features. Artificial Neural Network (ANN) applys the extracted features to detect the lane. The efficiency and accuracy of our system is evaluated by using the Caltech lane dataset. The average correct detection rate is 94.67% and the computational time is 17.64 ms/frame by applying the proposed method.