Real-Time Lane Detection on Suburban Streets Using Visual Cue Integration

Abstract

The detection of lane boundaries on suburban streets using images obtained from video constitutes a challenging task. This is mainly due to the difficulties associated with estimating the complex geometric structure of lane boundaries, the quality of lane markings as a result of wear, occlusions by traffic, and shadows caused by road-side trees and structures. Most of the existing techniques for lane boundary detection employ a single visual cue and will only work under certain conditions and where there are clear lane markings. Also, better results are achieved when there are no other onroad objects present. This paper extends our previous work and discusses a novel lane boundary detection algorithm specifically addressing the abovementioned issues through the integration of two visual cues. The first visual cue is based on stripe-like features found on lane lines extracted using a two-dimensional symmetric Gabor filter. The second visual cue is based on a texture characteristic determined using the entropy measure of the predefined neighbourhood around a lane boundary line. The visual cues are then integrated using a rulebased classifier which incorporates a modified sequential covering algorithm to improve robustness. To separate lane boundary lines from other similar features, a road mask is generated using road chromaticity values estimated from CIE L*a*b* colour transformation. Extraneous points around lane boundary lines are then removed by an outlier removal procedure based on studentized residuals. The lane boundary lines are then modelled with Bezier spline curves. To validate the algorithm, extensive experimental evaluation was carried out on suburban streets and the results are presented.

Dynamic Approach for Lane Detection using Google Street View and CNN

“…Similarly, certain distinctive test data-sets have been evaluated over different hyper-parameters to obtain the over-all accuracy of the model and the results had been tabulated in Table.I. We observed a significant improvement in the results in comparison to the existing methods like SVM, MLPC, as shown in Fig.6 and a few other adaptive algorithms, as in [1], [2].…”

“…The retrieval of images from Google Street-View is quite a powerful alternative rather than camera-based images in certain applications like drawing out the coverage of plantation in a region or detecting traffic signs/figures on the lane and take steps to improve the awareness. Mainly for the earlier stages of development huge image data-sets are available for many different countries all over the world (2) which can be accessed easily and efciently for further developments on them as depicted in this paper.…”

Efficient Lane Detection Based on Artificial Neural Networks

“…Many approaches for automatic lane detection have been reported in the literature: (Assidiq 2008), (Fernando 2014), (Saha 2012), (Van Winden 2016) and (Wang 2006), to mention a few.…”

“…They show that their proposal is robust to light changes and shadows by testing it with both painted and unpainted road as well as curved and straight road in different weather conditions. In (Fernando 2014), the authors face lane detection by integrating two visual cues: 1) stripe-like features found on lane lines extracted using a two-dimensional symmetric Gabor filter, and 2) texture characteristic determined using the entropy measure of a predefined neighbourhood around a lane boundary line. They integrate both visual cues using a rule-based classifier which incorporates a modified sequential covering algorithm to improve robustness.…”

MANIAC: uma metodologia para o monitoramento automatizado das condições dos pavimentos utilizando VANTs

“…Vários são os algoritmos propostos para detecção de pavimentos Birdal e Erçil (2007), Assidiq et al (2008), Yenikaya, Yenikaya e Düven (2013), Fernando et al (2014), Yinka et al (2014), entretanto, a maior parte dos algoritmos encontrados está focada na localização do pavimento a partir de lasers ou imagens obtidas a partir de câmeras instaladas em veículos terrestres, mais especi camente, para o uso em veículos terrestres autônomos. Esses algoritmos não funcionam quando se tem imagens obtidas a partir de veículos aéreos, uma vez que as informações da trajetória e dos ângulos utilizados não serem os mesmos.…”