Lane marking detection and classification with combined deep neural network for driver assistance

et al. 2022

IJECE

<p>Nowadays, researchers are incorporating many modern and significant features on advanced driver assistance systems (ADAS). Lane marking detection is one of them, which allows the vehicle to maintain the perspective road lane. Conventionally, it is detected through handcrafted and very specialized features and goes through substantial post-processing, which leads to high computation, and less accuracy. Additionally, this conventional method is vulnerable to environmental conditions, making it an unreliable model. Consequently, this research work presents a deep learning-based model that is suitable for diverse environmental conditions, including multiple lanes, different daytime, different traffic conditions, good and medium weather conditions, and so forth. This approach has been derived from plain encode-decode E-Net architecture and has been trained by using the differential and cross-entropy losses for the backpropagation. The model has been trained and tested using 3,600 training and 2,700 testing images from TuSimple, a robust public dataset. Input images from very diverse environmental conditions have ensured better generalization of the model. This framework has reached a max accuracy of 96.61%, with an F1 score of 96.34%, a precision value of 98.91%, and a recall of 93.89%. Besides, this model has shown very small false positive and false negative values of 3.125% and 1.259%, which bits the performance of most of the existing state of art models.</p>

Section: Related Workmentioning

confidence: 99%

Efficient lane marking detection using deep learning technique with differential and cross-entropy loss

et al. 2022

IJECE

“…Though lane marking detection is considered as the primary research topic for autonomous cars, it is quite tricky and challenging under distinct conditions and effects [15]. Consequently, researchers are trying to detect the lane marking more precisely in the recent past [16]. At the time of changing the road lanes, it causes a thousand of innocent people's death due to human errors.…”

Section: Related Workmentioning

confidence: 99%

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

2021

IJECE

<span>In recent times, many innocent people are suffering from sudden death for the sake of unwanted road accidents, which also riveting a lot of financial properties. The researchers have deployed advanced driver assistance systems (ADAS) in which a large number of automated features have been incorporated in the modern vehicles to overcome human mortality as well as financial loss, and lane markings detection is one of them. Many computer vision techniques and intricate image processing approaches have been used for detecting the lane markings by utilizing the handcrafted with highly specialized features. However, the systems have become more challenging due to the computational complexity, overfitting, less accuracy, and incapability to cope up with the intricate environmental conditions. Therefore, this research paper proposed a simple encode-decode deep learning model to detect lane markings under the distinct environmental condition with lower computational complexity. The model is based on SegNet architecture for improving the performance of the existing researches, which is trained by the lane marking dataset containing different complex environment conditions like rain, cloud, low light, curve roads. The model has successfully achieved 96.38% accuracy, 0.0311 false positive, 0.0201 false negative, 0.960 F1 score with a loss of only 1.45%, less overfitting and 428 ms per step that outstripped some of the existing researches. It is expected that this research will bring a significant contribution to the field lane marking detection.</span>

“…To make the model more robust, He et al introduced a global optimization by considering all other aspects of roads like lane orientation, probabilities length, and width [26]. In contrast, Zhang et al proposed Dynamic programming to make the model more robust by extracting the other objects and shadows from the trained output [27].…”

Section: False Detection Under Distinct Conditions 6 Post-processingmentioning

confidence: 99%

A Review on Lane Marking Detection Using Deep Neural Network

Computational Intelligence in Pattern Recognition

2021

Lane marking recognition is one of the most crucial features for automotive vehicles as it is the most preliminary requirement of all the autonomy features of the ADAS Systems. The researchers have made a promising improvement in the application of Lane Marking Detection (LMD) in recent times. This research article has taken the initiative to review LMD using deep learning techniques. This paper initially discusses the importance of LMD using Deep Neural Network (DNN) techniques. LMD processes can be categorized into four steps: the dataset, pre-processing, DNN networks, and post-processing. Besides, the network's architecture is described emphasizing particular groups of a neural network like CNN, E-Net, a combination of CNN and RNN, including their contributions and limitations. Besides, comparative performance result with visualization of the final output of five existing techniques is elaborated. Finally, this review is concluded by pointing to some particular challenges future directions for solving the issues.