Lightweight Multi-Scale Dilated U-Net for Crop Disease Leaf Image Segmentation
Abstract
:1. Introduction
- LWMSDU-Net is constructed by retaining local and multi-scale detail information;
- Dilated convolution is introduced into U-Net to enlarge the receptive field of the convolution layer, improve the feature learning ability of U-Net, and obtain more information about leaf spot image;
- A residual path (Respath) connection instead of the skip connection is employed to allow gradient information to flow better through the network and overcome gradient vanishing and degradation.
2. Related Works
2.1. Residual Block
2.2. Dilated Convolution
2.3. U-Net
2.4. Summarization
3. Lightweight Multi-Scale Dilated U-Net (LWMSDU-Net)
3.1. LWMSDU-Net Architecture
3.2. Process of CDLIS
4. Experiments and Analysis
4.1. Dataset
4.2. Results
4.3. Ablation Experiments and Results
5. Analysis and Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Disease Type | Number of Original Images | Number of Augmented Images | Total | |
---|---|---|---|---|
Corn | Leaf blight | 20 | 200 | 220 |
Brown spot | 20 | 200 | 220 | |
Cucumber | Target spot | 20 | 200 | 220 |
Brown spot | 20 | 200 | 220 | |
Anthracnose | 20 | 200 | 220 | |
Total number of images | 100 | 1000 | 1100 |
Method | U-Net | LWU-Net | AU-Net | MSU-Net | LWMSDU-Net |
---|---|---|---|---|---|
Precision | 86.13 | 89.86 | 92.54 | 93.25 | 94.18 |
Recall | 82.36 | 81.18 | 84.31 | 85.25 | 89.10 |
F1-score | 84.20 | 85.30 | 88.23 | 89.07 | 91.57 |
Pixel accuracy | 85.66 | 90.24 | 91.50 | 91.45 | 93.71 |
Training Time | 12.51 h | 6.42 h | 10.52 h | 11.14 h | 5.17 h |
Testing time | 5.64 s | 5.18 s | 5.42 s | 4.85 s | 4.73 s |
Combination Mode | Precision | Training Time |
---|---|---|
U-Net: 3 × 3 conv.+ Skip connection | 86.13 | 12.51 h |
U-Net: 3 × 3 conv. + Respath connection | 87.22 | 11.36 h |
Res-U-Net: residual block + Skip connection | 90.14 | 11.75 h |
Inception U-Net: Inception + Skip connection | 92.16 | 10.46 h |
U-Net: Inception module + Respath connection | 91.57 | 9.73 h |
U-Net: dilated Inception module + skip connection | 92.46 | 7.13 h |
LWMSDU-Net: dilated Inception + Respath connection | 94.18 | 5.17 h |
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Xu, C.; Yu, C.; Zhang, S. Lightweight Multi-Scale Dilated U-Net for Crop Disease Leaf Image Segmentation. Electronics 2022, 11, 3947. https://doi.org/10.3390/electronics11233947
Xu C, Yu C, Zhang S. Lightweight Multi-Scale Dilated U-Net for Crop Disease Leaf Image Segmentation. Electronics. 2022; 11(23):3947. https://doi.org/10.3390/electronics11233947
Chicago/Turabian StyleXu, Cong, Changqing Yu, and Shanwen Zhang. 2022. "Lightweight Multi-Scale Dilated U-Net for Crop Disease Leaf Image Segmentation" Electronics 11, no. 23: 3947. https://doi.org/10.3390/electronics11233947
APA StyleXu, C., Yu, C., & Zhang, S. (2022). Lightweight Multi-Scale Dilated U-Net for Crop Disease Leaf Image Segmentation. Electronics, 11(23), 3947. https://doi.org/10.3390/electronics11233947