Lane Recognition Algorithm Using the Hough Transform Based on Complicated Conditions

Abstract

At present, most lane line detection methods are aimed at simple road surface. There is still no good solution for the situation that the lane line contains arrow, text and other signs. The edge left by markers such as arrow and text will interfere with the detection of lane lines. In view of the situation of arrow mark and text mark interference between lane lines, the paper proposes a new processing algorithm. The algorithm consists of four parts, Gaussian blur, image graying processing, DLD-threshold (Dark-Light-Dark-threshold) algorithm, correlation filter edge extraction and Hough transform. Among them, the DLD-threshold algorithm and related filters are mainly used to remove the identification interference between lane lines. The test results on the Caltech Lanes dataset are given at the end of the article. The result of verification of this algorithm showed a max recognition rate of 97.2%.

Teknik Pemrosesan Gambar Digital pada Hasil Pengelasan TIG Aluminum Paduan untuk Aplikasi Pengukuran Lebar Manik Las

“…Metode terakhir yang diaplikasikan adalah hough trasnform, setelah seluruh proses awal (pre-processing method) pengolahan citra/gambar, metode ini dipakai untuk mengekstraksi dan membuat garis lurus pada tepi manik las. Untuk mengetahui detail teori hough transform dan beberapa contoh implementasinya bisa dilihat di referensi berikut [11,12].…”

Line segment detection algorithm in image extraction improvement study

“…The algorithm consists of four parts: Gaussian blurring, image graying process, DLD thresholding algorithm, correlation filtering edge extraction and Hough transform. According to the findings, the highest recognition rate of 97.2 % for interference recognition between lane lines on Caltech Lanes dataset [14]. Rectangular target detection is an improved algorithm based on straight LSD algorithm which is more widely used in real life applications.…”

Lane line detection at nighttime on fractional differential and central line point searching with Fragi and Hessian

“…For the new algorithm, compared with Canny or other differential operators, the algorithm does not generate too much noise and false edges, which lays a good foundation for the subsequent recursive Hough transform. The ninth row is for the corresponding recursive Hough line detection results 23 , 24 . The detection results can fully meet the requirements of lane line detection.…”

“…To overcome the shortages, some researchers have studied various straight line and curve fitting algorithms according to the characteristics of lane lines, for instance, Ozgunalp et al developed such an algorithm for tracing the lane lines based on the vanishing point estimation 19 ; Niu et al made the two-stage feature extraction with a curve fitting function 20 , and in 2021, we studied lane line detection in the raining weather with improved MSR and Hessian matrix for road image enhancement 21 . To well identify lane lines after the above image segmentation, the different versions of Hough transform are published, e.g., Fang, et al (2018) researched a lane line extraction algorithm on Hough Transform, in their Hough transform, the points conforming to the parallel characteristics, angle and length characteristics, and intercept characteristics of lane line are chosen in a Hough space, where, the selected points are converted into a lane line equation, and the final lane lines are conducted with fusion and property identification 22 ; Sun, et al (2019) made a multiple stage Hough Space computation for lane line detection 23 ; and Zhang and Ma (2019) did a lane line detection method by utilizing the Hough transform for the complex environments 24 .…”