• Title/Summary/Keyword: Traffic improvement

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Damaged Traffic Sign Recognition using Hopfield Networks and Fuzzy Max-Min Neural Network (홉필드 네트워크와 퍼지 Max-Min 신경망을 이용한 손상된 교통 표지판 인식)

  • Kim, Kwang Baek
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.26 no.11
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    • pp.1630-1636
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    • 2022

  • The results of current method of traffic sign detection gets hindered by environmental conditions and the traffic sign's condition as well. Therefore, in this paper, we propose a method of improving detection performance of damaged traffic signs by utilizing Hopfield Network and Fuzzy Max-Min Neural Network. In this proposed method, the characteristics of damaged traffic signs are analyzed and those characteristics are configured as the training pattern to be used by Fuzzy Max-Min Neural Network to initially classify the characteristics of the traffic signs. The images with initial characteristics that has been classified are restored by using Hopfield Network. The images restored with Hopfield Network are classified by the Fuzzy Max-Min Neural Network onces again to finally classify and detect the damaged traffic signs. 8 traffic signs with varying degrees of damage are used to evaluate the performance of the proposed method which resulted with an average of 38.76% improvement on classification performance than the Fuzzy Max-Min Neural Network.

  • https://doi.org/10.6109/jkiice.2022.26.11.1630 인용 PDF KSCI

A Study on Improvement of Maritime Traffic Analysis Using Shape Format Data for Maritime Autonomous Surface Ships (자율운항선박 도입을 위한 수치해도 데이터 활용 해상교통분석 개선방안)

  • Hwang, Taewoong;Hwang, Taemin;Youn, Ik-Hyun
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.28 no.6
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    • pp.992-1001
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    • 2022

  • The maritime traffic analysis has been conducted in various ways to solve problems arising from the complex marine environment. However, recent trends in the maritime industry, such as the development of the maritime autonomous surface ships (MASS), suggest that maritime traf ic analysis needs change. Accordingly, based on the studies conducted over the past decade for improvements, automatic identification system (AIS) data is mainly used for maritime traffic analysis. Moreover, the use of geographic information that directly af ects ship operation is relatively insufficient. Therefore, this study presented a method of using a combination of shape format data and AIS data to enhance maritime traffic analysis in preparation for the commercialization of autonomous ships. Consequently, extractable marine traffic characteristics were presented when shape format data were used for marine traffic analysis. This is expected to be used for marine traffic analysis for the introduction of autonomous ships in the future.

  • https://doi.org/10.7837/kosomes.2022.28.6.992 인용 PDF KSCI

Revolutionizing Traffic Sign Recognition with YOLOv9 and CNNs

  • Muteb Alshammari;Aadil Alshammari
    • International Journal of Computer Science & Network Security
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    • v.24 no.8
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    • pp.14-20
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    • 2024

  • Traffic sign recognition is an essential feature of intelligent transportation systems and Advanced Driver Assistance Systems (ADAS), which are necessary for improving road safety and advancing the development of autonomous cars. This research investigates the incorporation of the YOLOv9 model into traffic sign recognition systems, utilizing its sophisticated functionalities such as Programmable Gradient Information (PGI) and Generalized Efficient Layer Aggregation Network (GELAN) to tackle enduring difficulties in object detection. We employed a publically accessible dataset obtained from Roboflow, which consisted of 3130 images classified into five distinct categories: speed_40, speed_60, stop, green, and red. The dataset was separated into training (68%), validation (21%), and testing (12%) subsets in a methodical manner to ensure a thorough examination. Our comprehensive trials have shown that YOLOv9 obtains a mean Average Precision (mAP@0.5) of 0.959, suggesting exceptional precision and recall for the majority of traffic sign classes. However, there is still potential for improvement specifically in the red traffic sign class. An analysis was conducted on the distribution of instances among different traffic sign categories and the differences in size within the dataset. This analysis aimed to guarantee that the model would perform well in real-world circumstances. The findings validate that YOLOv9 substantially improves the precision and dependability of traffic sign identification, establishing it as a dependable option for implementation in intelligent transportation systems and ADAS. The incorporation of YOLOv9 in real-world traffic sign recognition and classification tasks demonstrates its promise in making roadways safer and more efficient.

  • https://doi.org/10.22937/IJCSNS.2024.24.8.2 인용 PDF