Deep learning using computer vision in self driving cars for lane and traffic sign detection

Kanagaraj, Nitin; Hicks, David; Goyal, Ayush; Tiwari, Sanju; Singh, Ghanapriya

doi:10.1007/s13198-021-01127-6

Cited by 27 publications

(9 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Next, Kanagaraj et al [25] show how to improve the efficiency of autonomous vehicles by using Convolutional Neural Networks with Spatial Transformer Networks and real-time lane detection. First, the pipeline converts a realtime image to grayscale and smoothes the edges with a Gaussian Blur to reduce noise.…”

Section: Ii) Deep Learning + Geometric Modellingmentioning

confidence: 99%

Lane Detection in Autonomous Vehicles: A Systematic Review

et al. 2023

View full text Add to dashboard Cite

One of the essential systems in autonomous vehicles for ensuring a secure circumstance for drivers and passengers is the Advanced Driver Assistance System (ADAS). Adaptive Cruise Control, Automatic Braking/Steer Away, Lane-Keeping System, Blind Spot Assist, Lane Departure Warning System, and Lane Detection are examples of ADAS. Lane detection displays information specific to the geometrical features of lane line structures to the vehicle's intelligent system to show the position of lane markings. This article reviews the methods employed for lane detection in an autonomous vehicle. A systematic literature review (SLR) has been carried out to analyze the most delicate approach to detecting the road lane for the benefit of the automation industry. One hundred and two publications from well-known databases were chosen for this review. The trend was discovered after thoroughly examining the selected articles on the method implemented for detecting the road lane from 2018 until 2021. The selected literature used various methods, with the input dataset being one of two types: self-collected or acquired from an online public dataset. In the meantime, the methodologies include geometric modeling and traditional methods, while AI includes deep learning and machine learning. The use of deep learning has been increasingly researched throughout the last four years. Some studies used stand-alone deep learning implementations for lane detection problems. Meanwhile, some research focuses on merging deep learning with other machine learning techniques and classical methodologies. Recent advancements imply that attention mechanism has become a popular combined strategy with deep learning methods. The use of deep algorithms in conjunction with other techniques showed promising outcomes. This research aims to provide a complete overview of the literature on lane detection methods, highlighting which approaches are currently being researched and the performance of existing state-of-the-art techniques. Also, the paper covered the equipment used to collect the dataset for the training process and the dataset used for network training, validation, and testing. This review yields a valuable foundation on lane detection techniques, challenges, and opportunities and supports new research works in this automation field. For further study, it is suggested to put more effort into accuracy improvement, increased speed performance, and more challenging work on various extreme conditions in detecting the road lane.

show abstract

Section: Ii) Deep Learning + Geometric Modellingmentioning

confidence: 99%

Lane Detection in Autonomous Vehicles: A Systematic Review

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Although, researchers did come up with newer versions of R-CNN (Girshick et al , 2014), which are fast R-CNN (Girshick, 2015), and faster R-CNN (Ren et al , 2016), they still have room for improvement in terms of both speed and accuracy for detecting image objects in real-time. Most existing object detection systems found in past work are either too slow, inefficient or inaccurate and thus are not found suitable to be sufficiently fast and effective enough to detect image objects (Rahul et al , 2019; Kanagaraj et al , 2021; Gaurav et al , 2021) in real-time for implementing fall detection, crash detection and social distance detection.…”

Section: Literature Surveymentioning

confidence: 99%

Object detection and activity recognition in video surveillance using neural networks

Payghode

Goyal

Bhan

et al. 2023

IJWIS

View full text Add to dashboard Cite

Purpose This paper aims to implement and extend the You Only Live Once (YOLO) algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. Video Surveillance has many applications such as Car Tracking and tracking of people related to crime prevention. This paper provides exhaustive comparison between the existing methods and proposed method. Proposed method is found to have highest object detection accuracy. Design/methodology/approach The goal of this research is to develop a deep learning framework to automate the task of analyzing video footage through object detection in images. This framework processes video feed or image frames from CCTV, webcam or a DroidCam, which allows the camera in a mobile phone to be used as a webcam for a laptop. The object detection algorithm, with its model trained on a large data set of images, is able to load in each image given as an input, process the image and determine the categories of the matching objects that it finds. As a proof of concept, this research demonstrates the algorithm on images of several different objects. This research implements and extends the YOLO algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. For video surveillance of traffic cameras, this has many applications, such as car tracking and person tracking for crime prevention. In this research, the implemented algorithm with the proposed methodology is compared against several different prior existing methods in literature. The proposed method was found to have the highest object detection accuracy for object detection and activity recognition, better than other existing methods. Findings The results indicate that the proposed deep learning–based model can be implemented in real-time for object detection and activity recognition. The added features of car crash detection, fall detection and social distancing detection can be used to implement a real-time video surveillance system that can help save lives and protect people. Such a real-time video surveillance system could be installed at street and traffic cameras and in CCTV systems. When this system would detect a car crash or a fatal human or pedestrian fall with injury, it can be programmed to send automatic messages to the nearest local police, emergency and fire stations. When this system would detect a social distancing violation, it can be programmed to inform the local authorities or sound an alarm with a warning message to alert the public to maintain their distance and avoid spreading their aerosol particles that may cause the spread of viruses, including the COVID-19 virus. Originality/value This paper proposes an improved and augmented version of the YOLOv3 model that has been extended to perform activity recognition, such as car crash detection, human fall detection and social distancing detection. The proposed model is based on a deep learning convolutional neural network model used to detect objects in images. The model is trained using the widely used and publicly available Common Objects in Context data set. The proposed model, being an extension of YOLO, can be implemented for real-time object and activity recognition. The proposed model had higher accuracies for both large-scale and all-scale object detection. This proposed model also exceeded all the other previous methods that were compared in extending and augmenting the object detection to activity recognition. The proposed model resulted in the highest accuracy for car crash detection, fall detection and social distancing detection.

show abstract

“…18 While in DL approaches, classification, and feature extraction are handled by the algorithm itself, eliminating the requirement for manual feature extraction. 19 There are mental healthcare strategies that incorporate stress awareness as a key component; however, these studies are catered to a certain audience. For instance, Lai et al 20 have proposed an efficient stress recognition aid for first responders and professionals, such as law enforcement officers, firefighters, explosive ordnance disposal operators, combat military personnel, emergency medical technicians, and paramedics, who deal with exceptional physical and psychological stressors.…”

Section: Related Workmentioning

confidence: 99%

Hybrid deep learning model for wearable sensor‐based stress recognition for Internet of Medical Things (IoMT) system

Singh,

Chetia Phukan,

Gupta

et al. 2023

Int J Communication

Self Cite

View full text Add to dashboard Cite

SummaryThere are numerous, interrelated, and multi‐dimensional aspects that influence a person's mental health, one of them being stress. Smart wearable technology having physiological and motion sensors has paved the way for real‐time data collection to deliver cutting‐edge information about the stress of individuals. It is now possible to build an Internet of Medical Things (IoMT) system that can recognise the user's stress, revealing the elements that cause stress. However, there are significant gaps in the existing system for stress recognition. To begin with, stress recognition is primarily studied for a specific set of people, such as occupational stress among office working persons or hospital staff during emergency duties, and so forth. Second, most past work on stress recognition has focused on extracting handcrafted traits, which necessitates human interaction and skill. To overcome above mentioned challenges, this work proposes a novel IoMT framework for continuous stress recognition for mental well‐being. This paper presents a hybrid deep learning (DL) approach for automatically retrieving features and classification into various stress states for the IoMT system to address these difficulties. The proposed expanded system gathers data from wearable physiological sensors and feeds it into a convolutional neural network–long short‐term memory (CNN‐LSTM), a hybrid DL classifier. The suggested approach has been tested on the wearable stress and affect detection (WESAD) dataset. It has a reported accuracy of 90.45%, which is higher than previously reported accuracies from existing machine learning (ML) and DL approaches.

show abstract

Deep learning using computer vision in self driving cars for lane and traffic sign detection

Cited by 27 publications

References 18 publications

Lane Detection in Autonomous Vehicles: A Systematic Review

Lane Detection in Autonomous Vehicles: A Systematic Review

Object detection and activity recognition in video surveillance using neural networks

Hybrid deep learning model for wearable sensor‐based stress recognition for Internet of Medical Things (IoMT) system

Contact Info

Product

Resources

About