Detecting Small Chinese Traffic Signs via Improved YOLOv3 Method

Zhang, Baojun; Wang, Guili; Hui-lan, Wang; Xu, Chenchen; Liu, Yu; Xu, Lin

doi:10.1155/2021/8826593

Cited by 7 publications

(6 citation statements)

References 21 publications

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“…e loss during training was recorded and plotted as a loss curve and compared with the model used in [1]. As can be seen from Figure 5, the YOLOv3 model [23][24][25] used in this study has a lower loss value and is better able to achieve the recognition of industrial meter types.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

A YOLOv3-Based Industrial Instrument Classification and Reading Recognition Method

Zhang¹,

Chen²,

Lei³

2022

Mobile Information Systems

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Aiming at the demand of industrial instrument reading, this study proposes a method of industrial instrument classification and reading recognition based on YOLOv3. Given that industrial meters can be divided into pointer meters and digital meters according to the dial type, this method conducts a reading study for each of the two types of meters. Firstly, the YOLOv3 model is trained to recognize and detect the meter types and classify the meters according to the values of the obtained classes. The pointer meter uses a Hough circle to detect the dial, extracts the scale and the pointer, calculates the angle between the 0 scale line and the pointer, and obtains the reading of the pointer meter. The digital meter extracts the digits by finding the contours of the dial and the digit area and then uses a support vector machine (SVM) to identify the extracted digits and output the readings of the digital meter. Through the test, the mean average precision (mAP) of the recognition model in this study is 93.73%. The absolute error of pointer meter reading is less than 0.1 in general, and the maximum relative error is 0.35%. The accuracy of the digital meter reading is 99.7%. The proposed method can accurately read the value of the instrument and meet the needs of industrial production.

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Section: Experimental Results and Analysismentioning

confidence: 99%

A YOLOv3-Based Industrial Instrument Classification and Reading Recognition Method

Zhang¹,

Chen²,

Lei³

2022

Mobile Information Systems

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“…The interior is identified using a trained SVM classifier in the final stage, which also sets the maximum allowed speed on this part of the route. Baojun Zhang, Guili Wang, Huilan Wang, Chenchen Xu, Yu Li, Lin Xu, in [2] aimed to increase an acceleration of long-range tiny target detection. The YOLOv3 network's three-scale detection structure is employed.…”

Section: Related Workmentioning

confidence: 99%

Traffic Sign Board Recognition and Voice Output System Using Yolo Model

2023

IRJMETS

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In recognition of our roadways to remain safe and effective, road signs are essential. As a result, we must carefully evaluate the strengths and weaknesses of automatic roads sign detection solutions. The variety and severity of difficult conditions are constrained in the databases of existing road signs. Due to concurrent modifications to various conditions, it is impossible to assess the impact of a single element because there is no metadata matching to these circumstances. In this study, we examine the TSDR issue in difficult conditions and concentrate on the functional degradation brought on by such circumstances. In order to do this, we suggest a prior enhancement-focused TSDR framework that is built on the YOLO principle. A YOLO-based challenge classifier, an encoder-decoder YOLO structure for image enhancement called Enhance-Net, and two different YOLO architectures for sign recognition and categorization make up our modular method. In the difficult photos that require their proper recognition, we suggest a novel learning workflow for Enhance-Net with an emphasis on improving the quality of the road sign sections (instead of the complete image). The CURE-TSD Real dataset, which is built on simulated testing settings that correlate to adversaries that can arise in real-world environments and systems, and a voice assistant were introduced to address the inadequacies in previous datasets.

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“…The dataset for traffic sign detection should consider as many situations as possible so that the model can be more robust and work well in a variety of situations. Although some teams [2] used their local datasets, most of them [3][4][5] used datasets called GTSDB German data set, tt100k data set, and CCTSDB dataset for the training the proposed algorithms or models. In the following subsections, these datasets that are usually used in this case are introduced in detail to illustrate their characteristics and advantages.…”

Section: Datasetmentioning

confidence: 99%

Traffic Sign Detection Based on Deep Learning Methods

Huang¹,

Wang²,

Yang³

et al. 2022

Proceedings of the 2022 3rd International Conference on Big Data and Informatization Education (ICBDIE 2022)

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This article gives a brief overview of a few current research on traffic signs detection, which briefly reviews the concept and structure of traffic signs detection in the last decade. The methodology varies in different ways which is generally separated into two exact dimensions. The first one is the traditional method using the theory of computer vision with machine learning to detect the traffic signs, while the other one uses deep learning to train the model to detect the objects. In recent years, the methods based on deep learning have gradually replaced the traditional methods since they can extract features from traffic signs better and do predictions. Therefore, this paper mainly focuses on the deep learning methods for traffic signs detection and reviews previous work and their corresponding datasets and performance. The results based on different methods are compared. Finally, we made a conclusion based on this review.

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Detecting Small Chinese Traffic Signs via Improved YOLOv3 Method

Cited by 7 publications

References 21 publications

A YOLOv3-Based Industrial Instrument Classification and Reading Recognition Method

A YOLOv3-Based Industrial Instrument Classification and Reading Recognition Method

Traffic Sign Board Recognition and Voice Output System Using Yolo Model

Traffic Sign Detection Based on Deep Learning Methods

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