Segmentation Method of Forbidden Traffic Signs Based on MSPCNN Model with Adjustable Parameters

Jing Di; Jinghui Wang; Jing Lian

doi:10.3788/LOP202158.0215002

Journals >Laser & Optoelectronics Progress >Volume 58 >Issue 2 >Page 0215002 > Article

Laser & Optoelectronics Progress
Vol. 58, Issue 2, 0215002 (2021)

Segmentation Method of Forbidden Traffic Signs Based on MSPCNN Model with Adjustable Parameters

Jing Di, Jinghui Wang^*, and Jing Lian

Author Affiliations

School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China

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DOI: 10.3788/LOP202158.0215002 Cite this Article Set citation alerts

Jing Di, Jinghui Wang, Jing Lian. Segmentation Method of Forbidden Traffic Signs Based on MSPCNN Model with Adjustable Parameters[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0215002 Copy Citation Text

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Fig. 1. Reddening preprocessing effect. (a) Original images; (b) corresponding reddening preprocessing effect

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Fig. 2. Experimental results of different methods under uniform illumination. (a) Original images; (b) PA-MSPCNN; (c) OTSU; (d) SPCNN; (e) PCNN

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Fig. 3. Experimental results of different methods under uneven illumination. (a) Original images; (b) PA-MSPCNN; (c) OTSU; (d) SPCNN; (e) PCNN

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Image name	Method	Recall rate	Precision rate	F-measure	Jaccard coefficient
3317	PA-MSPCNN	0.9942	0.2221	0.3631	0.2218
	OTSU	0.7855	0.2073	0.3281	0.1962
	SPCNN	0.7702	0.2068	0.3260	0.1948
	PCNN	0.9898	0.2182	0.3575	0.2177
5917	PA-MSPCNN	0.9043	0.9994	0.9495	0.9039
	OTSU	0.7622	0.9965	0.8637	0.7601
	SPCNN	0.7563	0.9976	0.8604	0.7549
	PCNN	0.9052	0.9858	0.9438	0.8936
42577	PA-MSPCNN	0.9980	0.5018	0.6678	0.5013
	OTSU	0.8275	0.9910	0.9019	0.8213
	SPCNN	0.9672	0.3890	0.5548	0.3839
	PCNN	0.9840	0.2674	0.4206	0.2663
90579	PA-MSPCNN	0.7282	0.8575	0.7876	0.6496
	OTSU	0.5607	0.9110	0.6941	0.5316
	SPCNN	0.5335	0.8848	0.6656	0.4988
	PCNN	0.5335	0.8917	0.6676	0.5010

Table 1. Evaluation metrics of four different images obtained by different methods under uniform illumination

Image name	Method	Recall rate	Precision rate	F-measure	Jaccard coefficient
6889	PA-MSPCNN	0.9980	0.4949	0.6617	0.4944
	OTSU	0.0039	0.0351	0.0071	0.0036
	SPCNN	0.2934	0.5091	0.3723	0.2287
	PCNN	0.2934	0.5091	0.3723	0.2287
15434	PA-MSPCNN	0.8259	0.7232	0.7712	0.6276
	OTSU	0.3024	0.8371	0.4443	0.2856
	SPCNN	0.2997	0.8314	0.4406	0.2825
	PCNN	0.2989	0.8311	0.4396	0.2817
Image name	Method	Recall rate	Precision rate	F-measure	Jaccard coefficient
33884	PA-MSPCNN	0.9424	0.8358	0.8859	0.7952
	OTSU	0.0996	0.8581	0.1785	0.0980
	SPCNN	0.0917	0.8592	0.1657	0.0903
	PCNN	0.0917	0.8592	0.1657	0.0903
45424	PA-MSPCNN	0.9889	0.7671	0.8640	0.7606
	OTSU	0.1807	0.9974	0.3060	0.1806
	SPCNN	0.1898	0.8514	0.3104	0.1837
	PCNN	0.1856	0.9928	0.3127	0.1854

Table 2. Evaluation metrics of four different images obtained by different methods under uneven illumination

Method	Recognition accuracy/%
OTSU	63
SPCNN	61
PCNN	60
PA-MSPCNN	85

Table 3. Recognition accuracy of different methods

Jing Di, Jinghui Wang, Jing Lian. Segmentation Method of Forbidden Traffic Signs Based on MSPCNN Model with Adjustable Parameters[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0215002

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