Anchor-Free Traffic Sign Recognition Algorithm Based on Attention Model

Fig. 6. Accuracy-recall curves of traffic signs at three scales. (a) Pixel interval of (0,32); (b) pixel interval of (32,96]; (c) pixel interval of (96,400]

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Fig. 7. Part of visual recognition results of AAFCNN model

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Table 1. Performance comparison of different traffic sign recognition methods

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Table 1. Performance comparison of different traffic sign recognition methods

Method	Backbone	Params /10⁶	Index	S /%	M /%	L /%
Faster R-CNN	ResNet-101	52.2	Recall	72.0	91.3	91.5
			Precision	76.1	87.5	86.1
			F1-score	74.0	89.4	88.7
Faster R-CNN +FPN	ResNet-101	60.1	Recall	86.6	95.5	95.1
			Precision	85.0	92.9	92.3
			F1-score	85.8	94.2	93.7
Ref. [15]		81.2	Recall	87.4	93.6	87.7
			Precision	81.7	90.8	90.6
			F1-score	84.5	92.0	89.1
RetinaNet	ResNeXt-101	94.7	Recall	87.4	95.1	93.1
			Precision	84.3	95.9	94.2
			F1-score	85.8	95.5	93.6
FCOS	ResNeXt-101	89.7	Recall	88.7	95.6	92.4
			Precision	85.6	96.4	93.5
			F1-score	86.8	96.0	93.0
CenterNet	HourglassNet	191.3	Recall	89.7	96.0	92.4
			Precision	90.1	96.7	94.9
			F1-score	89.9	96.3	93.6
AAFCNN	DenseNet-121	48.1	Recall	90.6	95.6	93.1
			Precision	91.2	97.3	96.8
			F1-score	90.9	96.4	94.9

Table 2. Effect of depth of densely connected network on recognition performance
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Table 2. Effect of depth of densely connected network on recognition performance
Backbone Params /10⁶ AP /%
S M L
DenseNet-121 48.1 63.4 80.1 86.1
DenseNet-169 65.4 62.5 79.9 86.1
DenseNet-201 101.4 61.7 79.7 85.7
DenseNet-264 154.8 61.9 80.0 85.0

Table 3. Effect of location of attention model on recognition performance
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Table 3. Effect of location of attention model on recognition performance
Location Params /10⁶ AP /%
S M L
In coding path 48.1 63.4 80.1 86.1
In decoding path 47.8 62.1 80.0 85.8
Both coding path and decoding path 48.2 62.6 80.0 85.1

Table 4. Performance comparison of each module
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Table 4. Performance comparison of each module
Model Params /10⁶ AP /%
S M L
Base 14.1 60.8 79.7 85.9
Base+AM 14.2 61.7 79.8 85.1
Base+SCP 47.8 61.9 80.0 87.2
Base+AM+SCP 48.1 63.4 80.1 86.1

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Jinghui Chu, Hao Huang, Wei Lü. Anchor-Free Traffic Sign Recognition Algorithm Based on Attention Model[J]. Laser & Optoelectronics Progress, 2021, 58(16): 1610020

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