Traffic sign detection and recognition based on pyramidal convolutional networks

Liang, Zhenwen; Shao, Jie; Zhang, Dongyang; Gao, Lianli

doi:10.1007/s00521-019-04086-z

Cited by 49 publications

(28 citation statements)

References 40 publications

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“…In order to verify our method from multiple aspects, we compared the methods commonly used today, such as traffic sign detection methods based on real environment [33], using pyramidal convolutional networks to detect traffic signs [5], Faster-rcnn, yolov3 [12], CornerNet [25] , CornerNet-Squeeze and CornerNet-Saccade [14]. At the same time, in order to verify the effectiveness of each module from multiple sizes.…”

Section: Resultsmentioning

confidence: 99%

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Anchor-free object detection with mask attention

Yang

Fan

Guo

2020

J Image Video Proc.

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The anchor-free method based on key point detection has made great progress. However, the anchor-free method is too dependent on using a convolutional network to generate a rough heatmap. This is difficult to detect for objects with a large size variation and dense and overlapping objects. To solve this problem, first, we propose a mask attention mechanism for object detection methods and make full use of the advantages of the attention mechanism to improve the accuracy of network detection heatmap generation. Then, we designed an optimized fire model to reduce the size of the model. The fire model is an extension of grouped convolution. The fire model allows each group of convolutional network features to learn the same feature through purposeful grouping. In this paper, the mask attention mechanism uses object segmentation images to guide the generation of corner heatmaps. Our approach achieved an accuracy of 91.84% and a recall of 89.83% in the Tencent-100 K dataset. Compared with the popular object detection methods, the proposed method has advantages in model size and accuracy.

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Section: Resultsmentioning

confidence: 99%

“…In recent years, applications based on object detection technology have become more widespread [1]. Common applications are pedestrian detection [2], vehicle detection [3], image retrieval [4], and traffic sign detection [5]. This imposes higher requirements on the detection performance and size of the object detection method.…”

Section: Introductionmentioning

confidence: 99%

Anchor-free object detection with mask attention

Yang

Fan

Guo

2020

J Image Video Proc.

View full text Add to dashboard Cite

show abstract

“…In order to verify our method from multiple aspects, we compared the methods commonly used today, such as traffic sign detection methods based on real environment [34], using pyramidal convolutional networks to detect traffic signs [5], Fasterrcnn [35], yolov3 [12], CornerNet [26] , CornerNet-Squeeze and CornerNet-Saccade [14]. At the same time, in order to verify the effectiveness of each module from multiple sizes.…”

Section: Resultsmentioning

confidence: 99%

“…In recent years, applications based on object detection technology have become more widespread [1]. Common applications such as pedestrian detection [2], vehicle detection [3], image retrieval [4], and traffic sign detection [5]. This imposes higher requirements on the detection performance and size of the object detection method.…”

Section: Introductionmentioning

confidence: 99%

Anchor free object detection with mask attention

Yang

Fan

Guo

2020

Preprint

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The anchor-free method based on key point detection has made great progress. However, the anchor-free method is too dependent on using a convolutional network to generate a rough heat map. This is difficult to detect for objects with a large size variation and dense and overlapping objects. To solve this problem, first, we propose a mask attention mechanism for object detection methods. And make full use of the advantages of the attention mechanism to improve the accuracy of network detection heat map generation. Then, we designed an optimized fire model to reduce the size of the model. The fire model is an extension of grouped convolution. The fire model allows each group of convolutional network features to learn the same feature through purposeful grouping. In this paper, the mask attention mechanism uses object segmentation images to guide the generation of corner heat maps. Our approach achieved an accuracy of 91.84% and a recall 89.83% in the Tencent-100K dataset. Compared with the popular object detection methods, the proposed method has advantages in model size and accuracy.

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“…Deep learning models such as convolutional neural networks (CNNs) have been designed especially for images and have shown excellent performance in many areas of computer vision and image processing [27]. Previously, CNNs of varying architectures have been widely used to recognize contents of standard traffic signs [1], [6] but their training requires a lot of data and specialized computational resources like Graphical Processing Units (GPUs).…”

Section: Related Workmentioning

confidence: 99%

Classification of Degraded Traffic Signs Using Flexible Mixture Model and Transfer Learning

et al. 2019

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Automatic detection and recognition of traffic signs is a topic of research for various applications like driver assistance, inventory management and autonomous driving. Poorly maintained traffic signs degrade by losing their colors or some part is weird due to aging and hence making the task more challenging. The problem is mainly related to the developing world and has gained less attention in the literature on automatic traffic sign detection and recognition. To handle the degradation issue, we present a novel flexible Gaussian mixture model based technique with automatic split and merge strategy. This adaptive scheme works as a preprocessing step which facilitates locating traffic signs under a certain degree of degradation in a real world scenario. A multiscale convolutional neural network augmented with dimensionality reduction layer is proposed to recognize contents of the sign. Since, there is no available benchmark dataset for this purpose, we collected a number of images containing partially degraded signs from the famous German Traffic Sign Detection Benchmark (GTSDB) and augmented it with manually and naturally degraded traffic sign images taken from the longest highway in the country of authors' residence. Experimental results show that our proposed technique outperforms many state of the art and recent methods for detection and recognition of degraded traffic signs. INDEX TERMS Degraded traffic sign, Gaussian mixture model, Transfer learning, Convolutional neural networks, dimensionality reduction.

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Traffic sign detection and recognition based on pyramidal convolutional networks

Cited by 49 publications

References 40 publications

Anchor-free object detection with mask attention

Anchor-free object detection with mask attention

Anchor free object detection with mask attention

Classification of Degraded Traffic Signs Using Flexible Mixture Model and Transfer Learning

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