Related references
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Summary: Traffic sign detection is essential for intelligent transportation and deep learning has been utilized successfully. This paper proposes a model with global feature extraction capabilities and a lightweight detection head to enhance the accuracy of small traffic sign detection. The proposed algorithm achieves an accuracy of 86.3%, recall of 82.1%, mAP@0.5 of 86.5%, mAP@0.5:0.95 of 65.6% on the TT100K dataset, with a stable frame rate of 73 frames per second for real-time detection.
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Nesrine Triki et al.
Summary: Artificial Intelligence (AI) is integrated with Advanced Driver Assistance Systems (ADAS) and/or Automated Driving Systems (ADS) in the automotive industry to produce intelligent and autonomous vehicles. This paper proposes an approach for Traffic Sign Recognition (TSR) in the automotive industry, aiming to build a real-time system that can analyze, detect, and classify traffic signs accurately. The proposed methodology combines the Haar cascade technique with a deep CNN model classifier, achieving high testing accuracy rates of 98.56% and 99.91%, and F1-measure rates of 99%. The developed TSR system is evaluated and validated on a Raspberry Pi 4 board, confirming its reliable performance.
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Computer Science, Artificial Intelligence
Yanting Zhang et al.
Summary: This study introduces a detection-by-tracking methodology to enhance the robustness and performance of traffic sign detection in videos by exploring temporal and spatial correlations. Experimental results demonstrate the effectiveness of this approach and its potential for generalization to other object detection tasks in videos.
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Summary: This study proposes a traffic sign recognition method based on semantic scene understanding and structural traffic sign location, which accurately detects traffic signs by establishing a scene structure model and using an improved object detection algorithm, thus overcoming the limitations of existing methods.
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Erfeng Gao et al.
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Jing Yu et al.
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Kan Xie et al.
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Xiang Gao et al.
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Summary: The proposed video object detection and recognition method has a wide range of applications in self-driving vehicles, intelligent transportation systems, and video surveillance. By employing data augmentation and improving the algorithm structure, our method has significantly enhanced detection performance and accuracy.
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Yuanyuan Liu et al.
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Jinghao Cao et al.
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Summary: This paper presents a deep learning small-object detection method based on image super-resolution, which improves the speed and accuracy of small-object detection by enhancing network structure and feature fusion methods. Experiment results show higher accuracy and speed compared to existing methods.
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