An effective automatic traffic sign classification and recognition deep convolutional networks

Mishra, Jayant; Goyal, Sachin

doi:10.1007/s11042-022-12531-w

Cited by 12 publications

(2 citation statements)

References 49 publications

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“…Compared with other methods, the mAP of the proposed method had a great improvement, with an increase ranging from 0.3% to 39.2%, which verified that the proposed ASFF-YOLOV5 algorithm can effectively improve the detection accuracy of multielement road traffic. Different from other scholars' research [40][41][42][43][44][45], this paper considered multiscale object detection, especially in small object detection whose detection accuracy was substantially improved. Compared with the results of different network models, the detection accuracy of the proposed method was improved both in terms of the overall accuracy and individual objects for detection, which proves the superiority of the proposed method for multiscale object detection.…”

Section: Discussionmentioning

confidence: 97%

ASFF-YOLOv5: Multielement Detection Method for Road Traffic in UAV Images Based on Multiscale Feature Fusion

2022

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Road traffic elements are important components of roads and the main elements of structuring basic traffic geographic information databases. However, the following problems still exist in the detection and recognition of road traffic elements: dense elements, poor detection effect of multi-scale objects, and small objects being easily affected by occlusion factors. Therefore, an adaptive spatial feature fusion (ASFF) YOLOv5 network (ASFF-YOLOv5) was proposed for the automatic recognition and detection of multiple multiscale road traffic elements. First, the K-means++ algorithm was used to make clustering statistics on the range of multiscale road traffic elements, and the size of the candidate box suitable for the dataset was obtained. Then, a spatial pyramid pooling fast (SPPF) structure was used to improve the classification accuracy and speed while achieving richer feature information extraction. An ASFF strategy based on a receptive field block (RFB) was proposed to improve the feature scale invariance and enhance the detection effect of small objects. Finally, the experimental effect was evaluated by calculating the mean average precision (mAP). Experimental results showed that the mAP value of the proposed method was 93.1%, which is 19.2% higher than that of the original YOLOv5 model.

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Section: Discussionmentioning

confidence: 97%

ASFF-YOLOv5: Multielement Detection Method for Road Traffic in UAV Images Based on Multiscale Feature Fusion

2022

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“…Mishra and Goyal (2022) [ 27 ] proposed a traffic sign classification and recognition method with the deep CNN. In the architecture of the proposed CNN model, there are convolutional layers, a pooling layer, and a max pooling layer with a range of dropouts.…”

Section: Deep Learning For Traffic Sign Recognitionmentioning

confidence: 99%

Recent Advances in Traffic Sign Recognition: Approaches and Datasets

Lim

Lee

Lim

et al. 2023

Sensors

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Autonomous vehicles have become a topic of interest in recent times due to the rapid advancement of automobile and computer vision technology. The ability of autonomous vehicles to drive safely and efficiently relies heavily on their ability to accurately recognize traffic signs. This makes traffic sign recognition a critical component of autonomous driving systems. To address this challenge, researchers have been exploring various approaches to traffic sign recognition, including machine learning and deep learning. Despite these efforts, the variability of traffic signs across different geographical regions, complex background scenes, and changes in illumination still poses significant challenges to the development of reliable traffic sign recognition systems. This paper provides a comprehensive overview of the latest advancements in the field of traffic sign recognition, covering various key areas, including preprocessing techniques, feature extraction methods, classification techniques, datasets, and performance evaluation. The paper also delves into the commonly used traffic sign recognition datasets and their associated challenges. Additionally, this paper sheds light on the limitations and future research prospects of traffic sign recognition.

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