RoadWay

Singal, Gaurav; Singhal, Himanshu; Kushwaha, Riti; Veeramsetty, Venkataramana; Badal, Tapas; Lamba, Sonu

doi:10.1007/s11042-022-12171-0

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…It may seem simple to analyze and detect visual information to obtain various information about the road. However, in fact, lane detection presents multiple challenges due to variations in the scene, weather and time of day (day and night), and camera angles [19].…”

Section: Lane Detectionmentioning

confidence: 99%

Investigation of the theory and applications of deep learning-based image recognition

Cai

2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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Image recognition technology, as an important branch in the field of artificial intelligence, aims to process and analyze images to recognize objects in images. The aim of this paper is to summarize the general approach to implementing image recognition and the application of this technology in some fields by analyzing relevant articles in the field of image recognition in recent years and to suggest possible conjectures on the future direction of this technology. This paper points out that the image recognition process is divided into two main parts: image processing and image recognition. For the implementation of the corresponding algorithms, this paper introduces two commonly used software libraries: OpenCV and YOLOv5. In addition, the paper also focuses on the applications and contributions of image recognition techniques to face recognition, lane detection, and medical image analysis. It is also pointed out that image recognition technology has a very promising future, and that there is still a lot of further improvement in the field of medical image analysis.

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Section: Lane Detectionmentioning

confidence: 99%

Investigation of the theory and applications of deep learning-based image recognition

Cai

2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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“…Yoo et al proposed a vanishing point estimation method based on a probabilistic voting procedure for detecting lanes in complex road environments [ 26 ]. Though these methods can detect lanes in good condition, their performance is unsatisfactory in various scenarios, including curved roads, rainy days, changing illumination conditions, blurry lane lines, and others [ 7 , 8 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, these methods fail to deliver a satisfactory performance when dealing with difficult road curvature, blurred lane lines, complex road patterns, etc. [ 7 , 8 ]. Researchers also applied model-based methods such as dynamic programming [ 9 ], Support Vector Machine (SVM) [ 10 ], B-Snake model [ 11 ], etc.…”

Section: Introductionmentioning

confidence: 99%

LLDNet: A Lightweight Lane Detection Approach for Autonomous Cars Using Deep Learning

Khan

Haque

Hasan

et al. 2022

Sensors

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Lane detection plays a vital role in making the idea of the autonomous car a reality. Traditional lane detection methods need extensive hand-crafted features and post-processing techniques, which make the models specific feature-oriented, and susceptible to instability for the variations on road scenes. In recent years, Deep Learning (DL) models, especially Convolutional Neural Network (CNN) models have been proposed and utilized to perform pixel-level lane segmentation. However, most of the methods focus on achieving high accuracy while considering structured roads and good weather conditions and do not put emphasis on testing their models on defected roads, especially ones with blurry lane lines, no lane lines, and cracked pavements, which are predominant in the real world. Moreover, many of these CNN-based models have complex structures and require high-end systems to operate, which makes them quite unsuitable for being implemented in embedded devices. Considering these shortcomings, in this paper, we have introduced a novel CNN model named LLDNet based on an encoder–decoder architecture that is lightweight and has been tested in adverse weather as well as road conditions. A channel attention and spatial attention module are integrated into the designed architecture to refine the feature maps for achieving outstanding results with a lower number of parameters. We have used a hybrid dataset to train our model, which was created by combining two separate datasets, and have compared the model with a few state-of-the-art encoder–decoder architectures. Numerical results on the utilized dataset show that our model surpasses the compared methods in terms of dice coefficient, IoU, and the size of the models. Moreover, we carried out extensive experiments on the videos of different roads in Bangladesh. The visualization results exhibit that our model can detect the lanes accurately in both structured and defected roads and adverse weather conditions. Experimental results elicit that our designed method is capable of detecting lanes accurately and is ready for practical implementation.

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