Lightweight Semantic Segmentation Network Based on Attention Coding

Table 1. Influence of APAM basis set number on segmentation accuracy
View table
Table 1. Influence of APAM basis set number on segmentation accuracy
Algorithm Base size mIoU /%
Baseline -- 69.8
Ours-1 36 83.3
Ours-2 49 83.4
Ours-3 64 83.5
Ours-4 81 83.6
Ours-5 100 83.6

Table 2. Segmentation results of different attention modules
View table
Table 2. Segmentation results of different attention modules
Algorithm Base size mIoU /%
MHA^[23] -- 59.95
SGE^[24] -- 58.90
PAM -- 70.71
CAM -- 70.53
Ours(average) 64 70.99
Ours(max) 64 69.93

Table 3. Structural analysis of the APAM
View table
Table 3. Structural analysis of the APAM
CF LCF mIoU /%
F1 concat 84.6
F2 concat 83.2
F3 concat 83.3
F4 concat 81.4
F4 sum 84.5
F5 sum 84.9
F6 sum 84.9

Table 4. Performance parameters of different algorithms
View table
Table 4. Performance parameters of different algorithms
Algorithm GFLOPS Params /M Memory /G mIoU /%
FCN 216.0 54.0 7.7 69.8
U-net 262.2 34.5 8.7 70.8
SegNet 244.6 32.4 8.9 71.1
DeepLab v2 251.7 44.6 8.0 71.6
PSP 254.7 67.6 8.1 80.9
DANet 275.4 68.5 9.3 85.1
Ours 228.8 67.7 8.1 84.9

Table 5. Classification results of different algorithms on the PASCAL VOC2012 validation set unit: %

View table

Table 5. Classification results of different algorithms on the PASCAL VOC2012 validation set unit: %

Algorithm	FCN	DeepLab v2	DPN^[25]	DeepLab v3+	PSP	ResNet38^[26]	EncNet^[27]	Ours
aero	82.4	84.4	87.7	88.0	87.4	94.4	94.1	91.6
bike	47.4	54.5	59.4	56.3	56.3	72.9	69.2	57.9
bird	81.2	81.5	78.4	86.3	85.7	94.9	96.3	90.0
boat	68.6	63.6	64.9	69.4	79.4	68.8	76.7	85.5
bottle	75.3	65.9	70.3	72.2	73.8	78.4	86.2	82.5
bus	81.3	85.1	89.3	90.3	92.3	90.6	96.3	95.0
car	79.9	79.1	83.5	85.7	87.3	90.0	90.7	90.8
cat	81.6	83.4	86.1	89.6	92.3	92.1	94.2	94.0
chair	33.7	30.7	31.7	28.9	53.3	40.1	38.8	53.8
cow	68.4	74.1	79.9	85.9	90.4	90.4	90.7	93.7
table	52.3	59.8	62.6	59.3	75.2	71.7	73.3	78.0
dog	76.4	79.0	81.9	84.2	87.3	89.9	90.0	93.0
horse	64.9	76.1	80.0	80.2	85.9	93.7	92.5	91.4
mbike	73.4	83.2	83.5	84.2	83.8	91.0	88.8	87.1
person	81.2	80.8	82.3	82.8	84.5	89.1	87.9	88.2
plant	56.7	59.7	60.5	56.0	68.1	71.3	68.7	74.1
sheep	69.7	82.2	83.2	78.5	87.0	90.7	92.6	91.3
sofa	50.9	50.4	53.4	51.6	73.0	61.3	59.0	75.8
train	78.5	73.1	77.9	84.5	91.1	87.7	86.4	92.8
tv	70.1	63.7	65.0	69.6	71.5	78.1	73.4	80.6
mIoU	69.8	71.6	74.1	75.1	80.9	82.5	82.9	84.9

Table 6. Classification results of different algorithms on the Cityscapes validation set unit: %

View table

Table 6. Classification results of different algorithms on the Cityscapes validation set unit: %

Algorithm	FCN	PSP	DenseASPP^[28]	DANet	Ours
road	95.1	96.4	97.3	97.2	97.4
sidewalk	67.8	74.4	78.1	77.8	79.4
building	88.5	89.1	89.5	89.8	90.0
wall	50.5	52.9	56.0	56.1	57.0
fence	44.6	47.9	48.5	48.6	51.1
pole	35.6	39.9	40.3	40.8	43.5
traffic light	47.0	51.9	52.8	53.0	53.4
Traffic sign	60.4	62.4	65.3	65.2	66.5
vegetation	88.6	89.4	89.6	89.7	89.8
terrain	55.6	57.6	60.5	60.7	60.9
sky	91.4	92.0	92.3	92.4	92.7
person	68.8	70.4	71.4	71.9	72.8
rider	47.9	49.9	52.0	52.2	53.4
car	90.3	91.4	92.1	92.4	92.4
truck	73.8	73.9	83.0	82.8	79.3
bus	73.6	75.8	80.2	79.4	81.9
train	62.8	66.4	70.4	70.8	74.3
motocycle	51.7	55.0	58.4	58.9	58.6
bicycle	63.1	63.6	65.5	65.8	66.6
mIoU	66.2	68.4	70.7	70.8	71.6

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Xiaolong Chen, Ji Zhao, Siyi Chen. Lightweight Semantic Segmentation Network Based on Attention Coding[J]. Laser & Optoelectronics Progress, 2021, 58(14): 1410012

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Paper Information

Category: Image Processing

Received: Sep. 16, 2020

Accepted: Nov. 14, 2020

Published Online: Jun. 30, 2021

The Author Email: Xiaolong Chen (350071235@qq.com), Siyi Chen (c.siyi@xtu.edu.cn)

DOI:10.3788/LOP202158.1410012

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Influence of APAM basis set number on segmentation accuracy

Table 1. Influence of APAM basis set number on segmentation accuracy

Table 2. Segmentation results of different attention modules

Table 2. Segmentation results of different attention modules

Table 3. Structural analysis of the APAM

Table 3. Structural analysis of the APAM

Table 4. Performance parameters of different algorithms

Table 4. Performance parameters of different algorithms

Table 5. Classification results of different algorithms on the PASCAL VOC2012 validation set unit: %

Table 5. Classification results of different algorithms on the PASCAL VOC2012 validation set unit: %

Table 6. Classification results of different algorithms on the Cityscapes validation set unit: %

Table 6. Classification results of different algorithms on the Cityscapes validation set unit: %