Fast and robust road sign detection in driver assistance systems

Zhang, Tao; Zou, Jie; Jia, Wenjing

doi:10.1007/s10489-018-1199-x

Cited by 11 publications

(4 citation statements)

References 35 publications

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“…When Nb is the total number of branches of the intelligent software [25] since the voltage level of the distribution network is low, the function that usually targets the balanced load can be expressed as the following formula: 4…”

Section: Music Automatic Notation Algorithmmentioning

confidence: 99%

Piano Performance and Music Automatic Notation Algorithm Teaching System Based on Artificial Intelligence

Yang

2021

Mobile Information Systems

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Artificial intelligence is a subject that studies all kinds of human intelligent activities and their laws. It is developed on the basis of the cohesion of many disciplines such as computer science, politics, information system, neurophysiology, psychology, philosophy, and language. This paper aims to study how to build a computer or intelligent machine, including hardware and software, imitate and expand the human brain to perform thinking functions such as thinking, programming, arithmetic, and learning, solve complex problems that need to be handled by professionals, and better apply the artificial intelligence assistance system to the teaching of piano performance. In this paper, Prolog language and music-assisted learning system based on the ARM and SA algorithm are proposed, the principle and operation process of music automatic recording technology are deeply studied, and the system data of artificial intelligence are summarized and analyzed by using internal database, so as to find out the implementation principle and law of piano automatic recording system. So that the artificial intelligence assistant system can be better applied to music teaching. The experimental results show that, in the teaching system of piano performance and music automatic notation algorithm, the utilization rate of artificial intelligence auxiliary technology has reached 56.81 and is growing rapidly. Therefore, we can find that the artificial intelligence assistant system plays an important role in the teaching system of piano performance and automatic music notation.

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Section: Music Automatic Notation Algorithmmentioning

confidence: 99%

Piano Performance and Music Automatic Notation Algorithm Teaching System Based on Artificial Intelligence

Yang

2021

Mobile Information Systems

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“…Traditional traffic sign detection methods detect traffic signs based on fixed colors (red, blue, yellow) and specific shapes (triangles, circles, rectangles, polygons). For example, the methods in [22][23][24] identify traffic signs based on color threshold segmentation and color space transformation. Literature [25,26] use the Hough transform method to identify traffic signs.…”

Section: Traffic Sign Detectionmentioning

confidence: 99%

Attention feature fusion network for small traffic sign detection

Wu¹,

Yang²,

Zhang³

et al. 2022

Eng. Res. Express

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Object detection has made great progress with the rise of convolutional neural networks in recent years. Traffic sign detection is a research hotspot for object detection tasks. The existing detection models have the problems of inaccurate positioning and low classification accuracy when detecting small traffic signs. To address these issues, in this paper, we propose a small traffic sign detection method based on YOLOv4. Specifically, we design an attention-based feature fusion module including attention spatial pyramid pooling (ASPP) and attention path aggregation networks (APAN). ASPP highlights useful small object information and suppresses invalid interference information in the background. APAN reduces information loss during feature fusion. A large number of experimental results on public datasets show that the method in this paper improves the detection performance of the model. In terms of small traffic sign detection, the method improves YOLOv4 by 12 mAP, and meets the real-time requirements of automatic driving detection (more than 50 FPS).

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“…Traditional traffic sign detection algorithms are mainly concentrated on color segmentation, combining features such as the shape and contour for feature extraction, and then realizing the recognition of traffic sign by completing feature classification through classifiers [1][2][3][4][5][6]. The handmade features in traditional techniques are human exhaustion and a lack of sufficient robustness to deal with complex and changeable traffic environments.…”

Section: Introductionmentioning

confidence: 99%

STC-YOLO: Small Object Detection Network for Traffic Signs in Complex Environments

Lai

Chen

Liu

et al. 2023

Sensors

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The detection of traffic signs is easily affected by changes in the weather, partial occlusion, and light intensity, which increases the number of potential safety hazards in practical applications of autonomous driving. To address this issue, a new traffic sign dataset, namely the enhanced Tsinghua-Tencent 100K (TT100K) dataset, was constructed, which includes the number of difficult samples generated using various data augmentation strategies such as fog, snow, noise, occlusion, and blur. Meanwhile, a small traffic sign detection network for complex environments based on the framework of YOLOv5 (STC-YOLO) was constructed to be suitable for complex scenes. In this network, the down-sampling multiple was adjusted, and a small object detection layer was adopted to obtain and transmit richer and more discriminative small object features. Then, a feature extraction module combining a convolutional neural network (CNN) and multi-head attention was designed to break the limitations of ordinary convolution extraction to obtain a larger receptive field. Finally, the normalized Gaussian Wasserstein distance (NWD) metric was introduced to make up for the sensitivity of the intersection over union (IoU) loss to the location deviation of tiny objects in the regression loss function. A more accurate size of the anchor boxes for small objects was achieved using the K-means++ clustering algorithm. Experiments on 45 types of sign detection results on the enhanced TT100K dataset showed that the STC-YOLO algorithm outperformed YOLOv5 by 9.3% in the mean average precision (mAP), and the performance of STC-YOLO was comparable with that of the state-of-the-art methods on the public TT100K dataset and CSUST Chinese Traffic Sign Detection Benchmark (CCTSDB2021) dataset.

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Fast and robust road sign detection in driver assistance systems

Cited by 11 publications

References 35 publications

Piano Performance and Music Automatic Notation Algorithm Teaching System Based on Artificial Intelligence

Piano Performance and Music Automatic Notation Algorithm Teaching System Based on Artificial Intelligence

Attention feature fusion network for small traffic sign detection

STC-YOLO: Small Object Detection Network for Traffic Signs in Complex Environments

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