A Vision-Based Lane Detection Approach for Autonomous Vehicles Using a Convolutional Neural Network Architecture

Khan, Md. Al-Masrur; Kee, Seong‐Hoon; Sikder, Niloy; Mamun, Md. Abdullah Al; Zohora, Fatima Tuz; Hasan, Md. Mahedi; Bairagi, Anupam Kumar

doi:10.1109/icievicivpr52578.2021.9564229

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…These images were later used in a U-shaped network for accurate lane detection [ 29 ]. Khan et al employed a fully convolutional network (FCN) architecture incorporating a secondary layer protection scheme for detecting the lanes as well as ensuring their prototype vehicle is always kept inside the track [ 30 ]. Chng et al improved a lane detection method named RONELD by making it more robust to detect lane changes.…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

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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“…The aspiration of DL algorithms comes from the operational structure of the human brain [10] and is capable of processing high dimensional data such as images, video, etc. It reduces our burden for performing complex tasks such as pattern recognition, classification, disease detection, and language analysis [6,7,11,12]. Recently, it has also been rigorously used in the Agri-domain, especially for the detection of plant disease; crop prediction; plant categorization; pest range; and pesticide impact assessment [1,6,13,14].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Sunflower Diseases Using Hybrid Deep Learning and Its Explainability with AI

et al. 2023

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Sunflower is a crop that has many economic values and ornamental usages. However, its production can be hampered due to various diseases such as downy mildew, gray mold, and leaf scars, and it is challenging for farmers to identify disease-prone conditions with traditional approaches. Thus, a computerized model composed of vision, artificial intelligence, and machine learning is the demand of the age to detect diseases in plants efficiently. In this paper, we develop a hybrid model with transfer learning (TL) and a simple CNN using a small dataset for detecting sunflower diseases. Out of the eight models tested on the dataset of four different classes (downy mildew, gray mold, leaf scars, and fresh leaf), the VGG19 + CNN hybrid model achieves the best results in terms of precision, recall, F1-score, accuracy, Hamming loss, Matthews coefficient, Jaccard score, and Cohen’s kappa metrics. The experimental outcomes show that the proposed model provides better precision, recall, and accuracy than other approaches on the benchmark dataset.

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Trajectory Tracking Control of Wheeled Mobile Robots Using Neural Networks and Feedback Control Techniques

Cruz

Rodríguez

Aguilar

et al. 2023

Studies in Computational Intelligence

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A Vision-Based Lane Detection Approach for Autonomous Vehicles Using a Convolutional Neural Network Architecture

Cited by 4 publications

References 24 publications

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

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

Recognition of Sunflower Diseases Using Hybrid Deep Learning and Its Explainability with AI

Trajectory Tracking Control of Wheeled Mobile Robots Using Neural Networks and Feedback Control Techniques

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