Traffic Sign Recognition for Autonomous Vehicle Using Optimized YOLOv7 and Convolutional Block Attention Module

Kuppusamy, P.; Sanjay, M.; Deepashree, P. V.; Iwendi, C.

doi:10.32604/cmc.2023.042675

Cited by 3 publications

(2 citation statements)

References 33 publications

(37 reference statements)

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“…This study implies that YOLOv3 and Faster R-CNN perform comparatively better for road sign detection. In [17], authors proposed a detection model to detect and identify traffic signs based on YOLOv7 and Convolutional Block Attention Module. In study [1], authors proposed a road sign detection and recognition system based onYOLOv5s object detection algorithm.…”

Section: Related Workmentioning

confidence: 99%

US Road Sign Detection and Visibility Estimation using Artificial Intelligence Techniques

AbuKhait

2024

IJACSA

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This paper presents a fully-automated system for detecting road signs in the United States and assess their visibility during daytime from the perspective of the driver using images captured by an in-vehicle camera. The system deploys YOLOv8 to build a multi-label detection model and then, calculates various readability and detectability factors, including the simplicity of the surroundings, potential obstructions, and the angle at which the road sign is positioned, to determine the overall visibility of the sign. This proposed system can be integrated into Driver Assistance Systems (DAS) to manage the information delivered to drivers, as an excess of information could potentially distract them. Road signs are categorized based on their visibility levels, allowing Driver Assistance Systems to caution drivers about signs that may have lower visibility but are of significant importance. The system comprises four main stages: 1) identifying road signs using YOLOv8; 2) segmenting the surrounding areas; 3) measuring visibility parameters; and 4) determining visibility levels through fuzzy logic inference system. This paper introduces a visibility estimation system for road signs specifically tailored to the United States. Experimental results showcase the system's effectiveness. The visibility levels generated by the proposed system were subjectively compared to decisions made by human experts, revealing a substantial agreement between the two approaches.

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Section: Related Workmentioning

confidence: 99%

US Road Sign Detection and Visibility Estimation using Artificial Intelligence Techniques

AbuKhait

2024

IJACSA

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“…When various items are stored irregularly and their placement is obscured by obstructions, inspectors are easy to false detection and missing detections. Faced with complex regulatory environment and high customs clearance pressures, it is necessary to replace manual visual recognition with automatic detection to achieve intelligent recognition of contraband images [2].…”

Section: Introductionmentioning

confidence: 99%

Research on Enhanced Contraband Dataset ACXray Based on ETL

Song,

Yang,

Zhang

et al. 2024

CMC

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To address the shortage of public datasets for customs X-ray images of contraband and the difficulties in deploying trained models in engineering applications, a method has been proposed that employs the Extract-Transform-Load (ETL) approach to create an X-ray dataset of contraband items. Initially, X-ray scatter image data is collected and cleaned. Using Kafka message queues and the Elasticsearch (ES) distributed search engine, the data is transmitted in real-time to cloud servers. Subsequently, contraband data is annotated using a combination of neural networks and manual methods to improve annotation efficiency and implemented mean hash algorithm for quick image retrieval. The method of integrating targets with backgrounds has enhanced the X-ray contraband image data, increasing the number of positive samples. Finally, an Airport Customs X-ray dataset (ACXray) compatible with customs business scenarios has been constructed, featuring an increased number of positive contraband samples. Experimental tests using three datasets to train the Mask Region-based Convolutional Neural Network (Mask R-CNN) algorithm and tested on 400 real customs images revealed that the recognition accuracy of algorithms trained with Security Inspection X-ray (SIXray) and Occluded Prohibited Items X-ray (OPIXray) decreased by 16.3% and 15.1%, respectively, while the ACXray dataset trained algorithm's accuracy was almost unaffected. This indicates that the ACXray dataset-trained algorithm possesses strong generalization capabilities and is more suitable for customs detection scenarios.

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A Review on India Traffic Sign Detection Techniques

Kaushal Pravinbhai Patel,

Dr. Sheshang Degadwala

2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

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This abstract delves into the realm of traffic sign detection techniques tailored for India's diverse and dynamic traffic environment. It navigates through traditional methods like color segmentation and template matching, juxtaposing them with the contemporary prowess of deep learning, particularly convolutional neural networks (CNNs). The intricacies of Indian roads, encompassing varied signage designs, fluctuating lighting conditions, and complex infrastructural nuances, are scrutinized in the context of these detection mechanisms. The narrative extends to discuss the amalgamation of IoT devices, real-time processing frameworks, and vehicle-mounted cameras to forge more efficient detection systems. Furthermore, the review underscores the transformative impact of machine learning advancements, spotlighting transfer learning and ensemble techniques as instrumental in augmenting detection accuracy and scalability. This abstract encapsulates a comprehensive exploration of India's traffic sign detection landscape, offering insights into ongoing trends, persistent challenges, and promising avenues for future research and development.

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Traffic Sign Recognition for Autonomous Vehicle Using Optimized YOLOv7 and Convolutional Block Attention Module

Cited by 3 publications

References 33 publications

US Road Sign Detection and Visibility Estimation using Artificial Intelligence Techniques

US Road Sign Detection and Visibility Estimation using Artificial Intelligence Techniques

Research on Enhanced Contraband Dataset ACXray Based on ETL

A Review on India Traffic Sign Detection Techniques

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