RoadWay

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

doi:10.1007/s11042-022-12171-0

Cited by 14 publications

(5 citation statements)

References 21 publications

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“…This method is comparable to GCLNet in many ways. The innovative and efficient approach provided by Gaurav Singal et al [32] shows a high degree of accuracy in lane recognition while keeping a low execution time. The model's dimensions were purposefully kept low to guarantee hardware compatibility and to allow for realtime execution.…”

Section: Learning-based Methodsmentioning

confidence: 99%

A Study on Vision Based Lane Detection Methods for Advanced Driver Assistance Systems

S D,

C J

2023

IJCERT

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Intelligent driving systems need to find the lines that show where the lanes are present. It can help drivers prevent lane hopping and improve vehicle positioning and identification by providing information about the current road conditions. The lane detection encounters several obstacles, including harsh illumination conditions, missing lane markings, and impediments. Due to their outstanding performance, Artificial Intelligence, machine learning, and deep learning-based algorithms have recently attracted considerable interest in the intelligent driving society. In this paper, we thoroughly analyses different lane detection approaches for lane detection including deep learning based techniques. In addition, we review known datasets about lanes and assessment criteria. It ends with a discussion of current problems and possible directions for a lane detection system.

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Section: Learning-based Methodsmentioning

confidence: 99%

A Study on Vision Based Lane Detection Methods for Advanced Driver Assistance Systems

S D,

C J

2023

IJCERT

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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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“…Deep learning has showcased remarkable capabilities in image identi cation, leading to solutions in various domains such as self-driving cars [17][18][19], biomedical applications [20][21][22] and defects detection [23,24]. While deep learning requires large dataset for learning, several methods have been developed to address the challenge of limited datasets.…”

Section: Deep Learningmentioning

confidence: 99%

An automated fluorescent defect detection system in precision castings by convolutional neural network

Yu,

KANG,

Ding

et al. 2023

Preprint

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Fluorescent Penetrant Inspection (FPI) is a widely used Non-Destructive Testing (NDT) method in the aerospace industry to inspect precision casting components. Currently, FPI inspection relies on visual examination, which can lead to challenges in distinguishing between defects and false indications. Moreover, human factors introduce variability in the results, impacting the consistency and reliability of the inspection process. This highlights the desirability of the automation of FPI to increase consistency, reliability and productivity. The deep learning method is gradually replacing the traditional approaches that involve image processing and machine learning classifiers in automated defect detection system. Deep learning method offers automatic feature extraction and high robustness, which contribute to more accurate and efficient defect detection. The use of various convolutional neural networks (CNN) in defect detection for flat superalloy plates processed with FPI and photographed to create digital images was investigated. Among the CNN models, MobileNetV2 exhibited outstanding performance, with a remarkable recall rate of 99.2% and an accuracy of 99.2%. Additionally, the effect of dataset imbalance on model performance was carefully examined. Moreover, the features extracted by the model are visualized using Guided Grad-CAM to reveal the attention of the CNN model to the fluorescent display features. These results underscore the strong capability of deep learning architectures in detect defects in aerospace precision casting components, paving the way for the automation of the entire FPI process.

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RoadWay

Cited by 14 publications

References 21 publications

A Study on Vision Based Lane Detection Methods for Advanced Driver Assistance Systems

A Study on Vision Based Lane Detection Methods for Advanced Driver Assistance Systems

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

An automated fluorescent defect detection system in precision castings by convolutional neural network

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