Low-Parameter Real-Time Image Segmentation Algorithm Based on Convolutional Neural Network

Guanghong Tan; Jin Hou; Yanpeng Han; Shuo Luo

doi:10.3788/LOP56.091003

Laser & Optoelectronics Progress, Volume. 56, Issue 9, 091003(2019)

Low-Parameter Real-Time Image Segmentation Algorithm Based on Convolutional Neural Network

Guanghong Tan, Jin Hou^*, Yanpeng Han, and Shuo Luo

Author Affiliations

School of Information Science and Technology, Southwest Jiaotong University, Chengdu, Sichuan 611756, China

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Figures & Tables(11)

Fig. 1. Convolution kernel. (a) Classical convolution kernel; (b) dilated convolution kernel Rrate=2; (c) dilated convolution kernel Rrate=3

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Fig. 2. Atrous-Fire modular structure

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Fig. 3. Dilated convolution kernel and initial characteristic graphs. (a) Sawtooth structure convolution kernel; (b) no grid feature graph; (c) grid feature graph

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Fig. 4. Network structure of Atrous-squeezeseg

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Fig. 5. Training loss value curves

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Fig. 6. Validation loss value curves

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Fig. 7. Effect comparison of ADE20K. (a) Original images; (b) ground truth; (c) proposed algorithm; (d) Squeezeseg+FCN; (e) VGG16+FCN; (f) SqueezeNet+FCN; (g) without dilated; (h) without BN

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Fig. 8. Effect comparison of PASCAL VOC. (a) Original images; (b) ground truth; (c) proposed algorithm; (d) Squeezeseg+FCN; (e) VGG16+FCN; (f) SqueezeNet+FCN; (g) without dilated; (h) without BN

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Table 1. Encoder parameters

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Table 1. Encoder parameters

Layer name	Output size	Squeeze(S1)	Expand(E1/E3)	R_rate
Input image	224×224×3
Conv1	112×112×64
Maxpool1	56×56×64
Atrous-Fire1 (3×)	56×56×256	16	32	2/5/7
Maxpool2	28×28×256
Atrous-Fire2 (3×)	28×28×256	32	64	2/3/5
Maxpool3	14×14×256
Atrous-Fire3 (3×)	14×14×256	64	128	2/3/5
Atrous-Fire4 (2×)	14×14×512	128	256	1/2
Atrous-Fire4 (2×)	14×14×512	128	256	1/1

Table 2. Number of parameters of different semantic segmentation models and MIU

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Table 2. Number of parameters of different semantic segmentation models and MIU

Method	Number ofparameters	M_IU	Building	Sky	Car	Tree	Road	Person	Floor	Wall
Atrous-squeezeseg	21.09	62.9	67.5	84.0	61.4	58.1	64.7	49.1	60.4	58.5
Squeezeseg+FCN	54.65	55.9	61.8	85.8	48.4	51.8	61.5	32.3	53.8	52.2
VGG16+FCN	66.21	63.2	68.3	86.8	61.1	58.2	66.0	48.5	58.3	57.4
SqueezeNet+FCN	54.65	50.5	46.7	83.8	44.7	51.8	55.5	28.0	47.3	46.7
Atrous-squeezeseg(without dilated)	21.09	50.6	51.1	83.5	41.2	43.8	53.8	29.7	51.4	50.1
Atrous-squeezeseg(without BN)	21.09	51.6	51.3	83.5	43.6	45.8	58.0	29.8	50.1	51.3

Table 3. PA and FPS of model in different devices

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Table 3. PA and FPS of model in different devices

Method	FPS /(frame·s^-1)		P_A /%
Method		GTX 1080Ti	NVIDIA TX2
Atrous-squeezeseg	45.3	8.3	59.5
Squeezeseg+FCN	39.5	4.2	59.3
VGG16+FCN	29.6	1.9	59.8
SqueezeNet+FCN	46.6	4.5	55.6
Atrous-squeezeseg(without dilated)	45.6	8.4	56.1
Atrous-squeezeseg(without BN)	56.2	9.2	57.3

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Guanghong Tan, Jin Hou, Yanpeng Han, Shuo Luo. Low-Parameter Real-Time Image Segmentation Algorithm Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(9): 091003

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Paper Information

Category: Image Processing

Received: Oct. 22, 2018

Accepted: Nov. 30, 2018

Published Online: Jul. 5, 2019

The Author Email: Hou Jin (jhou@swjtu.edu.cn)

DOI:10.3788/LOP56.091003

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology