Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (10): 3058-3066.DOI: 10.11772/j.issn.1001-9081.2023101424
• Artificial intelligence • Previous Articles Next Articles
Qiumei ZHENG, Weiwei NIU(), Fenghua WANG, Dan ZHAO
Received:
2023-10-23
Revised:
2024-02-28
Accepted:
2024-03-08
Online:
2024-10-15
Published:
2024-10-10
Contact:
Weiwei NIU
About author:
ZHENG Qiumei, born in 1964, professor. Her research interests include computer vision, image processing, digital watermarking.Supported by:
通讯作者:
牛薇薇
作者简介:
郑秋梅(1964—),女,山东东营人,教授,主要研究方向:计算机视觉、图像处理、数字水印基金资助:
CLC Number:
Qiumei ZHENG, Weiwei NIU, Fenghua WANG, Dan ZHAO. Dual-branch real-time semantic segmentation network based on detail enhancement[J]. Journal of Computer Applications, 2024, 44(10): 3058-3066.
郑秋梅, 牛薇薇, 王风华, 赵丹. 基于细节增强的双分支实时语义分割网络[J]. 《计算机应用》唯一官方网站, 2024, 44(10): 3058-3066.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023101424
基线 网络 | DEBIM | LDAFF | 边界 损失 | mIoU/% | 参数量/MB | 帧速率/ (frame·s-1) |
---|---|---|---|---|---|---|
√ | 77.1 | 5.71 | 101.6 | |||
√ | √ | 77.6 | 5.71 | 100.3 | ||
√ | √ | √ | 77.8 | 5.73 | 92.3 | |
√ | √ | √ | √ | 78.2 | 5.73 | 92.3 |
Tab. 1 Influence of proposed modules on algorithm performance
基线 网络 | DEBIM | LDAFF | 边界 损失 | mIoU/% | 参数量/MB | 帧速率/ (frame·s-1) |
---|---|---|---|---|---|---|
√ | 77.1 | 5.71 | 101.6 | |||
√ | √ | 77.6 | 5.71 | 100.3 | ||
√ | √ | √ | 77.8 | 5.73 | 92.3 | |
√ | √ | √ | √ | 78.2 | 5.73 | 92.3 |
ADD | FFM | LDAFF | mIoU/% | 帧速率/(frame·s-1) |
---|---|---|---|---|
√ | 77.6 | 100.32 | ||
√ | 77.6 | 81.40 | ||
√ | 78.2 | 92.30 |
Tab. 2 Influence of different feature fusion methods on algorithm performance
ADD | FFM | LDAFF | mIoU/% | 帧速率/(frame·s-1) |
---|---|---|---|---|
√ | 77.6 | 100.32 | ||
√ | 77.6 | 81.40 | ||
√ | 78.2 | 92.30 |
β | mIoU/% | β | mIoU/% |
---|---|---|---|
0.10 | 77.9 | 0.50 | 77.9 |
0.20 | 78.1 | 0.75 | 77.8 |
0.25 | 78.2 | 1.00 | 77.6 |
Tab. 3 Influence of boundary loss weight on algorithm performance
β | mIoU/% | β | mIoU/% |
---|---|---|---|
0.10 | 77.9 | 0.50 | 77.9 |
0.20 | 78.1 | 0.75 | 77.8 |
0.25 | 78.2 | 1.00 | 77.6 |
方法 | 输入图像尺寸 | 参数量/MB | GPU | mIoU/% | 帧速率/(frame·s-1) | |
---|---|---|---|---|---|---|
验证集 | 测试集 | |||||
ICNet[ | 2 048×1 024 | 26.50 | TitanX M | — | 69.5 | 30.0 |
BiSeNet[ | 1 536×768 | 5.80 | GTX 1080Ti | 69.0 | 68.4 | 105.8 |
BiSeNetV2[ | 1 024×512 | — | GTX 1080Ti | 73.4 | 72.6 | 156.0 |
STDC1-Seg75[ | 1 536×768 | 14.20 | GTX 2080Ti | 74.5 | 75.3 | 74.6 |
STDC2-Seg75[ | 1 536×768 | 22.20 | GTX 2080Ti | 77.0 | 76.8 | 73.5 |
PP-LiteSeg-T2[ | 1 536×768 | — | GTX 2080Ti | 76.0 | 74.9 | 91.5 |
PP-LiteSeg-B2[ | 1 536×768 | — | GTX 2080Ti | 77.8 | 77.1 | 79.1 |
HLFGNet[ | 2 048×1 024 | 50.53 | GTX 2080Ti | 76.6 | 75.4 | 75.0 |
MSFNet[ | 2 048×1 024 | — | GTX 2080Ti | — | 77.1 | 41.0 |
SGCPNet[ | 2 048×1 024 | 0.61 | GTX 2080Ti | — | 70.9 | 106.5 |
DDRNet-23-slim[ | 2 048×1 024 | 5.71 | GTX 2080Ti | 77.1 | 77.4 | 101.6 |
RTFormer-slim[ | 2 048×1 024 | 4.80 | GTX 2080Ti | 76.1 | 75.4 | 89.6 |
DEDBNet | 2 048×1 024 | 5.73 | GTX 2080Ti | 78.2 | 77.8 | 92.3 |
Tab. 4 Comparison results of different methods on Cityscapes dataset
方法 | 输入图像尺寸 | 参数量/MB | GPU | mIoU/% | 帧速率/(frame·s-1) | |
---|---|---|---|---|---|---|
验证集 | 测试集 | |||||
ICNet[ | 2 048×1 024 | 26.50 | TitanX M | — | 69.5 | 30.0 |
BiSeNet[ | 1 536×768 | 5.80 | GTX 1080Ti | 69.0 | 68.4 | 105.8 |
BiSeNetV2[ | 1 024×512 | — | GTX 1080Ti | 73.4 | 72.6 | 156.0 |
STDC1-Seg75[ | 1 536×768 | 14.20 | GTX 2080Ti | 74.5 | 75.3 | 74.6 |
STDC2-Seg75[ | 1 536×768 | 22.20 | GTX 2080Ti | 77.0 | 76.8 | 73.5 |
PP-LiteSeg-T2[ | 1 536×768 | — | GTX 2080Ti | 76.0 | 74.9 | 91.5 |
PP-LiteSeg-B2[ | 1 536×768 | — | GTX 2080Ti | 77.8 | 77.1 | 79.1 |
HLFGNet[ | 2 048×1 024 | 50.53 | GTX 2080Ti | 76.6 | 75.4 | 75.0 |
MSFNet[ | 2 048×1 024 | — | GTX 2080Ti | — | 77.1 | 41.0 |
SGCPNet[ | 2 048×1 024 | 0.61 | GTX 2080Ti | — | 70.9 | 106.5 |
DDRNet-23-slim[ | 2 048×1 024 | 5.71 | GTX 2080Ti | 77.1 | 77.4 | 101.6 |
RTFormer-slim[ | 2 048×1 024 | 4.80 | GTX 2080Ti | 76.1 | 75.4 | 89.6 |
DEDBNet | 2 048×1 024 | 5.73 | GTX 2080Ti | 78.2 | 77.8 | 92.3 |
类别 | BiSeNet[ | DDRNet-23-slim[ | DEDBNet |
---|---|---|---|
mIoU | 74.5 | 77.1 | 78.2 |
road | 98.2 | 98.1 | 98.2 |
sidewalk | 83.2 | 84.4 | 85.4 |
building | 91.6 | 92.1 | 92.5 |
wall | 45.0 | 56.8 | 58.1 |
fence | 50.7 | 60.2 | 61.9 |
pole | 62.0 | 62.7 | 63.6 |
traffic light | 71.3 | 68.7 | 69.5 |
traffic sign | 74.6 | 76.6 | 76.6 |
vegetation | 92.8 | 92.1 | 92.3 |
terrain | 70.4 | 66.7 | 64.9 |
sky | 94.9 | 94.6 | 94.6 |
person | 83.4 | 80.8 | 80.6 |
rider | 66.2 | 62.1 | 59.4 |
car | 94.9 | 94.8 | 94.9 |
truck | 61.4 | 80.3 | 83.3 |
bus | 75.5 | 85.7 | 89.5 |
train | 67.0 | 78.8 | 80.8 |
motorcycle | 61.2 | 53.8 | 61.9 |
bicycle | 72.3 | 74.6 | 75.7 |
Tab. 5 Accuracy for each category on Cityscapes test set
类别 | BiSeNet[ | DDRNet-23-slim[ | DEDBNet |
---|---|---|---|
mIoU | 74.5 | 77.1 | 78.2 |
road | 98.2 | 98.1 | 98.2 |
sidewalk | 83.2 | 84.4 | 85.4 |
building | 91.6 | 92.1 | 92.5 |
wall | 45.0 | 56.8 | 58.1 |
fence | 50.7 | 60.2 | 61.9 |
pole | 62.0 | 62.7 | 63.6 |
traffic light | 71.3 | 68.7 | 69.5 |
traffic sign | 74.6 | 76.6 | 76.6 |
vegetation | 92.8 | 92.1 | 92.3 |
terrain | 70.4 | 66.7 | 64.9 |
sky | 94.9 | 94.6 | 94.6 |
person | 83.4 | 80.8 | 80.6 |
rider | 66.2 | 62.1 | 59.4 |
car | 94.9 | 94.8 | 94.9 |
truck | 61.4 | 80.3 | 83.3 |
bus | 75.5 | 85.7 | 89.5 |
train | 67.0 | 78.8 | 80.8 |
motorcycle | 61.2 | 53.8 | 61.9 |
bicycle | 72.3 | 74.6 | 75.7 |
方法 | GPU | mIoU/% | 帧速率/(frame·s-1) |
---|---|---|---|
ICNet[ | TitanX | 67.1 | 27.8 |
BiSeNet1[ | GTX 1080Ti | 65.6 | 175.0 |
BiSeNet2[ | GTX 1080Ti | 68.7 | 116.3 |
BiSeNetV2[ | GTX 1080Ti | 72.4 | 124.5 |
BiSeNetV2-L[ | GTX 1080Ti | 73.2 | 32.7 |
STDC1-Seg[ | RTX 2080Ti | 73.0 | 125.6 |
STDC2-Seg[ | RTX 2080Ti | 73.9 | 100.5 |
HLFGNet[ | RTX 2080Ti | 70.9 | 96.2 |
MSFNet[ | RTX 2080Ti | 75.4 | 91.0 |
SGCPNet[ | RTX 2080Ti | 69.0 | 278.4 |
DDRNet-23-slim[ | RTX 2080Ti | 74.7 | 217.0 |
DEDBNet | RTX 2080Ti | 79.2 | 202.8 |
Tab. 6 Performance comparison of different methods on CamVid test set
方法 | GPU | mIoU/% | 帧速率/(frame·s-1) |
---|---|---|---|
ICNet[ | TitanX | 67.1 | 27.8 |
BiSeNet1[ | GTX 1080Ti | 65.6 | 175.0 |
BiSeNet2[ | GTX 1080Ti | 68.7 | 116.3 |
BiSeNetV2[ | GTX 1080Ti | 72.4 | 124.5 |
BiSeNetV2-L[ | GTX 1080Ti | 73.2 | 32.7 |
STDC1-Seg[ | RTX 2080Ti | 73.0 | 125.6 |
STDC2-Seg[ | RTX 2080Ti | 73.9 | 100.5 |
HLFGNet[ | RTX 2080Ti | 70.9 | 96.2 |
MSFNet[ | RTX 2080Ti | 75.4 | 91.0 |
SGCPNet[ | RTX 2080Ti | 69.0 | 278.4 |
DDRNet-23-slim[ | RTX 2080Ti | 74.7 | 217.0 |
DEDBNet | RTX 2080Ti | 79.2 | 202.8 |
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