Dynamic Approach for Lane Detection using Google Street View and CNN

Abstract

Lane detection algorithms have been the key enablers for a fully-assistive and autonomous navigation systems. In this paper, a novel and pragmatic approach for lane detection is proposed using a convolutional neural network (CNN) model based on SegNet encoder-decoder architecture. The encoder block renders low-resolution feature maps of the input and the decoder block provides pixel-wise classification from the feature maps. The proposed model has been trained over 2000 image data-set and tested against their corresponding groundtruth provided in the data-set for evaluation. To enable real-time navigation, we extend our model's predictions interfacing it with the existing Google APIs evaluating the metrics of the model tuning the hyper-parameters. The novelty of this approach lies in the integration of existing segnet architecture with google APIs. This interface makes it handy for assistive robotic systems. The observed results show that the proposed method is robust under challenging occlusion conditions due to pre-processing involved and gives superior performance when compared to the existing methods.

Lane marking detection using simple encode decode deep learning technique: SegNet

“…The proposed method is considered a simple encode-decode deep learning technique based on SegNet architecture for lane markings detection, which is very significant for semantic segmentation [27]. The layout of the proposed method architecture has been shown in Figure 2.…”

“…In the modern era, the majority of the methods are slow [25] though the fastest ways [26] are not precise enough to implement. In the recent period, many researchers have been employed different deep neural network techniques for lane marking [27]. Tabelini et al has introduced a PolyLaneNet to detect lane marks to use in real-time applications.…”

An efficient encode-decode deep learning network for lane markings instant segmentation

“…For lane markings detection, the proposed technique uses a simple encode-decode deep learning approach based on SegNet architecture, and that is very efficient for semantic segmentation [18]. The proposed technique's layout is depicted in Figure 1.…”

“…Since the architecture of SegNet has a smaller number of weights, Mamidala et al proposed an encoder-decoder CNN focused on it. By adding successive convolutional and de-convolutional layers, the model was able to achieve an accuracy of about 96.1% [18]. A novel neural network has been introduced as embedding loss driven generative adversarial network considering the computational cost and classification problem in a pixel-wise approach.…”

A Review on Lane Marking Detection Using Deep Neural Network

“…Mamidala et al [16] proposed a Convolution Neural Network based on the modification of SegNet to overcome the challenges under different occlusion conditions and real-time implementation. CNN has very few weights of SegNet architecture, usually performed as an encoder-decoder procedure.…”