[논문]Traffic Signal Recognition System Based on Color and Time for Visually Impaired

P. Kamakshi

doi:10.22937/ijcsns.2023.23.4.7

Traffic Signal Recognition System Based on Color and Time for Visually Impaired

International journal of computer science and network security : IJCSNS, v.23 no.4, 2023년, pp.48 - 54

P. Kamakshi (Kakatiya Institute of Technology & Science Warangal)

Abstract ▼ AI-Helper

Nowadays, a blind man finds it very difficult to cross the roads. They should be very vigilant with every step they take. To resolve this problem, Convolutional Neural Networks(CNN) is a best method to analyse the data and automate the model without intervention of human being. In this work, a traffic signal recognition system is designed using CNN for the visually impaired. To provide a safe walking environment, a voice message is given according to light state and timer state at that instance. The developed model consists of two phases, in the first phase the CNN model is trained to classify different images captured from traffic signals. Common Objects in Context (COCO) labelled dataset is used, which includes images of different classes like traffic lights, bicycles, cars etc. The traffic light object will be detected using this labelled dataset with help of object detection model. The CNN model detects the color of the traffic light and timer displayed on the traffic image. In the second phase, from the detected color of the light and timer value a text message is generated and sent to the text-to-speech conversion model to make voice guidance for the blind person. The developed traffic light recognition model recognizes traffic light color and countdown timer displayed on the signal for safe signal crossing. The countdown timer displayed on the signal was not considered in existing models which is very useful. The proposed model has given accurate results in different scenarios when compared to other models.

주제어

표/그림 (11)

그림 Figure 1: Flow chart of the proposed model
그림 Figure 2: CNN Model
표 Table 1:Comparison of different Datasets
그림 Figure 3: R-CNN model for traffic signal recognition
그림 Figure 4: Overview of Proposed Method System Design
그림 Figure 5: Labelling different classes using COCO dataset.
그림 Figure 6: Traffic Signal Image
그림 Figure 7: Cropped Traffic signal image
그림 Figure 8: Detected traffic signal and timer
그림 Figure 9: Precision vs Recall graph
표 Table 2: Comparison of Our Proposed Model with other models

참고문헌 (17)

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상세보기
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상세보기
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