Identification of traffic signs for advanced driving assistance systems in smart cities using deep learning

Dhawan, Kshitij; R, Srinivasa Perumal; Nadesh, R. K.

doi:10.1007/s11042-023-14823-1

Cited by 10 publications

(5 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For addressing effects of weather conditions on intelligent parking recognition, Ensemble method depend on DL was developed in [14]. Custom CNN model was used in [15] to categorize traffic signs with higher accuracy. An approach for traffic sign recognition was developed in [16] specifically designed for complex urban road environments.…”

Section: Literature Studymentioning

confidence: 99%

Cox Regressive Outlier Robust Incremental Extreme Learning Machine based IoT aware Smart Car Parking and Route Optimization for Intelligent Transportation

V. Subapriya

2024

jes

View full text Add to dashboard Cite

An Intelligent Transport System (ITS) is a wide-ranging framework that influences advanced technologies to improve efficiency of transportation networks. An ITS integrated with an IoT enhances the capabilities of transportation networks. Owing to quick development of vehicles, traffic congestion during road network is major problems. Therefore, intelligent traffic control system is required to create smart transportation through optimization of vehicle route, and so on. In this paper, Cox Regressive Fuzzified Outlier Robust Incremental Extreme Learning Machine (CRFORIELM) is developed for smart ITS. The IoT technology in IT’S is used to collect traffic data, vehicle information, Parking slot occupancy data. After data collection process, proposed CRFORIELM technique includes two major processing steps namely parking slot availability detection and traffic aware route optimization. The extreme learning machine includes three types of layers such as input, three hidden layers as well as output layer. Initial hidden layer predicts the parking slot availability using Cox regression is performed. With the parking lot availability predictions, integrate this information into a route optimization algorithm. This algorithm considers the real-time traffic conditions as well as parking availability for detecting optimal route by applying fuzzy triangular membership function in the second hidden layer. Finally, nearest route path is effectively determined to minimize travel time in intelligent transport system. Experimental analysis indicates that the CRFORIELM technique achieved 2%, 5%, 10% improvement in accuracy, sensitivity, specificity and reduced error rates, route detection time by 39%, and 36%, compared to existing methods.

show abstract

Section: Literature Studymentioning

confidence: 99%

Cox Regressive Outlier Robust Incremental Extreme Learning Machine based IoT aware Smart Car Parking and Route Optimization for Intelligent Transportation

V. Subapriya

2024

jes

View full text Add to dashboard Cite

show abstract

“…The proposed hybrid model proves significantly faster, making it well-suited for real-world applications, surpassing existing CNNs and ViTs in speed and accuracy [20]. Recently, many deep learning-based methods have been implemented for traffic sign recognition and classification, as discussed in [21,22,23,24]. While existing research provides solutions to some of these issues, addressing adverse conditions and weather challenges is crucial for ensuring the system's effectiveness in diverse and changing situations.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Model Combining Depthwise Separable Convolutions and Vision Transformers for Traffic Sign Classification Under Challenging Weather Conditions.

Parse,

Pramod,

Kumar

2024

Preprint

View full text Add to dashboard Cite

This research presents a novel deep-learning framework designed for traffic sign image classification under adverse conditions, including rain, shadows, haze, codec errors, and dirty lenses. To effectively balance accuracy and training parameters, the approach combines depthwise and pointwise convolutions, often referred to as depthwise separable convolutions, with a Vision Transformer (ViT) for subsequent feature extraction. The framework's initial block comprises two pairs of depthwise and pointwise convolutional layers followed by a normalization layer. Depthwise convolution is responsible for processing each input channel independently and applying separate filters to each channel, thereby reducing computational cost and parameters while maintaining spatial structure. Pointwise convolutional layers combine information from different channels, fostering complex feature interactions and non-linearities. Batch normalization is used for training stability. At the end of the initial block, the max pooling layer is used to enhance and downsample spatial dimensions. The architecture repeats four times, preserving crucial information through skip connections. To extract global context information, inter-block skip connections and global average pooling (GAP) are employed for dimensionality reduction while retaining vital information. Integration of the ViT model in the final layers captures far-reaching dependencies and relations in the feature maps. The framework concludes with two fully connected layers, a bottleneck layer with 1024 neurons and a second layer using softmax activation to generate a probability distribution over 14 classes. The proposed framework, combining convolution blocks and skip connections with precisely tuned ViT hyperparameters, enhances model performance and achieves an exceptional validation accuracy of 99.3%.

show abstract

“…Dhawan has [7] created a novel methodology for traffic sign identification by investigating the impact of various color spaces on the convolutional neural network's capacity to effectively learn and depict images. The analysis of the deep perception KELM (DP-KELM) involved the utilization of a KELM classifier, which is a machine learning model based on kernel methods.…”

Section: Fig 6 Presentation Threats On the Fingerprint Biometric Modelmentioning

confidence: 99%

A Review of Pattern Recognition and Machine Learning

Adugna,

Ramu,

Haldorai

2024

JMC

View full text Add to dashboard Cite

This article aims to provide a concise overview of diverse methodologies employed at different stages of a pattern recognition system, highlighting contemporary research challenges and applications in this dynamic field. The integration of machine learning techniques has played a pivotal role in converging pattern recognition frameworks in academic literature. The process relies heavily on supervised or unsupervised categorization methods to achieve its objectives, with a notable focus on statistical approaches. More recently, there is a growing emphasis on incorporating neural network methodologies and insights from statistical learning theory. Designing an effective recognition system necessitates careful consideration of various factors, including pattern representation, pattern class definition, feature extraction, sensing environment, feature selection, classifier learning and design, cluster analysis, test and training sample selection, and performance assessment.

show abstract

Identification of traffic signs for advanced driving assistance systems in smart cities using deep learning

Cited by 10 publications

References 28 publications

Cox Regressive Outlier Robust Incremental Extreme Learning Machine based IoT aware Smart Car Parking and Route Optimization for Intelligent Transportation

Cox Regressive Outlier Robust Incremental Extreme Learning Machine based IoT aware Smart Car Parking and Route Optimization for Intelligent Transportation

A Hybrid Model Combining Depthwise Separable Convolutions and Vision Transformers for Traffic Sign Classification Under Challenging Weather Conditions.

A Review of Pattern Recognition and Machine Learning

Contact Info

Product

Resources

About