DEPANet: A Differentiable Edge-Guided Pyramid Aggregation Network for Strip Steel Surface Defect Segmentation
Abstract
:1. Introduction
- We propose a novel DEFP network consisting of stacked AFSFs to extract multiscale edge components from strip steel surface defects. Each AFSF utilizes a learnable Laplacian operator for edge feature extraction and a learnable Gaussian blurring mechanism for feature smoothing and enhancement.
- We propose EFAMs to generate edge-enhanced features, with each EFAM designed to integrate the backbone features with the edge components obtained from DEFP using a CBAM to enhance defect segmentation accuracy.
- Based on DEFP and EFAM, a novel network called DEPANet is proposed. Experimental results show that DEPANet achieves superior performance in segmenting and localizing complex defects on strip steel surfaces.
2. Related Work
2.1. ResNet-Based Methods
2.2. Edge Enhancement
3. Methods
3.1. Overall Architecture
3.2. Differentiable Edge Feature Pyramid Network (DEFP)
3.3. Edge-Aware Feature Aggregation Modules (EFAMs)
3.4. Loss Function
4. Experiment and Results
4.1. Datasets
4.2. Experimental Setup
4.3. Evaluation Metrics
4.4. Comparison Results
4.5. Visualization Analysis
4.6. Ablation Studies
4.7. Generalization Experiments
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Methods | MAE ↓ | SM ↑ | w- ↑ | ↑ | ↑ | ↑ | ↑ | ↑ | ↑ | Params (M) ↓ | FPS↑ |
---|---|---|---|---|---|---|---|---|---|---|---|
2LSG [32] | 0.2547 | 0.5723 | 0.2816 | 0.6745 | 0.5439 | 0.6572 | 0.4365 | 0.3954 | 0.4562 | 61.7 | 20.2 |
BASNet [33] | 0.0159 | 0.9193 | 0.8955 | 0.9593 | 0.9603 | 0.9714 | 0.8694 | 0.9011 | 0.9223 | 52.1 | 23.1 |
BC [34] | 0.1593 | 0.5861 | 0.3999 | 0.7264 | 0.6403 | 0.6836 | 0.4677 | 0.4227 | 0.4708 | —— | 18.0 |
BMPM [35] | 0.0312 | 0.8513 | 0.8454 | 0.9298 | 0.8972 | 0.9301 | 0.8384 | 0.8417 | 0.8622 | 49.9 | 26.2 |
CPD [36] | 0.0242 | 0.8981 | 0.7928 | 0.9299 | 0.9369 | 0.9744 | 0.8305 | 0.8701 | 0.9091 | 29.6 | 19.2 |
DACNet [37] | 0.0129 | 0.9321 | 0.9139 | 0.9671 | 0.9693 | 0.9627 | 0.8911 | 0.9217 | 0.9351 | 98.2 | 15.7 |
DSS [38] | 0.0256 | 0.8202 | 0.8296 | 0.9339 | 0.8514 | 0.9326 | 0.8581 | 0.8033 | 0.8501 | 74.9 | 24.6 |
EDRNet [39] | 0.0139 | 0.9343 | 0.9187 | 0.9635 | 0.9716 | 0.9790 | 0.8858 | 0.9177 | 0.9370 | 49.4 | 27.9 |
F3Net [40] | 0.0157 | 0.9155 | 0.9065 | 0.9680 | 0.9671 | 0.9724 | 0.8943 | 0.9038 | 0.9178 | 35.8 | 23.5 |
ITSD [41] | 0.0158 | 0.9201 | 0.8976 | 0.9561 | 0.9641 | 0.9733 | 0.8606 | 0.8923 | 0.9207 | 48.1 | 18.0 |
MIL [42] | 0.1854 | 0.6143 | 0.3087 | 0.7216 | 0.5971 | 0.7079 | 0.4760 | 0.4472 | 0.5385 | 56.5 | 20.8 |
MINet [43] | 0.0150 | 0.9202 | 0.9181 | 0.9604 | 0.9631 | 0.9701 | 0.8813 | 0.9011 | 0.8979 | 48.3 | 19.6 |
NLDF [44] | 0.0491 | 0.8021 | 0.6911 | 0.671 | 0.8050 | 0.8734 | 0.7211 | 0.7007 | 0.7621 | 56.3 | 23.4 |
PFANet [45] | 0.0876 | 0.7381 | 0.5322 | 0.7195 | 0.7500 | 0.8549 | 0.5545 | 0.5922 | 0.7089 | 88.0 | 17.6 |
PiCANet [46] | 0.0289 | 0.8912 | 0.8902 | 0.8961 | 0.9199 | 0.9659 | 0.7580 | 0.8278 | 0.8022 | 49.1 | 26.7 |
PoolNet [47] | 0.0244 | 0.9005 | 0.8322 | 0.9173 | 0.9311 | 0.9692 | 0.8062 | 0.8484 | 0.8905 | 54.4 | 25.0 |
R3Net [48] | 0.0263 | 0.8374 | 0.8301 | 0.9298 | 0.9022 | 0.9341 | 0.8419 | 0.8280 | 0.8351 | 48.3 | 27.0 |
RCRR [49] | 0.2455 | 0.5311 | 0.2317 | 0.6345 | 0.5465 | 0.6261 | 0.3327 | 0.3381 | 0.3923 | —— | 20.0 |
SAMNet [50] | 0.0267 | 0.9013 | 0.8491 | 0.9270 | 0.9385 | 0.9700 | 0.8045 | 0.8577 | 0.9027 | 65.5 | 29.3 |
SMD [51] | 0.2088 | 0.5808 | 0.3652 | 0.7093 | 0.5921 | 0.6467 | 0.4411 | 0.4233 | 0.4612 | 74.1 | 19.1 |
Ours | 0.0132 | 0.9373 | 0.9247 | 0.9793 | 0.9765 | 0.9793 | 0.9223 | 0.9193 | 0.9256 | 47.6 | 22.3 |
Exp. | DEPANet | SD-Saliency-900 | ||||||
---|---|---|---|---|---|---|---|---|
ResNet34 | DEFP | EFAM | CBAM | MAE ↓ | SM ↑ | ↑ | ↑ | |
#1 | ✓ | X | X | X | 0.0191 | 0.8891 | 0.9231 | 0.8919 |
#2 | ✓ | ✓ | ✓ | X | 0.0141 | 0.9267 | 0.9671 | 0.9298 |
#3 | ✓ | ✓ | ✓ | ✓ | 0.0132 | 0.9373 | 0.9758 | 0.9396 |
#4 | BCE | 0.0154 | 0.9015 | 0.9598 | 0.9116 | |||
#5 | Dice | 0.0149 | 0.9141 | 0.9674 | 0.9180 | |||
#6 | BCE + Dice | 0.0132 | 0.9373 | 0.9758 | 0.9396 |
Methods | MAE ↓ | SM ↑ | ↑ | ↑ | Parameters (M) ↓ |
---|---|---|---|---|---|
C2FNet [53] | 0.0207 | 0.8982 | 0.9701 | 0.8835 | 26.4 |
CPD [36] | 0.0254 | 0.8884 | 0.9627 | 0.8777 | 29.6 |
DACNet [37] | 0.0226 | 0.8855 | 0.9570 | 0.8722 | 98.2 |
EDRNet [39] | 0.0229 | 0.8651 | 0.9603 | 0.8769 | 49.4 |
BC [34] | 0.1356 | 0.6004 | 0.6704 | 0.4976 | — |
F3Net [40] | 0.0208 | 0.8977 | 0.9663 | 0.8910 | 35.8 |
MINet [43] | 0.0221 | 0.8885 | 0.9604 | 0.8798 | 48.3 |
RCRR [49] | 0.2194 | 0.5633 | 0.6198 | 0.4377 | — |
CorrNet [54] | 0.0243 | 0.8776 | 0.9532 | 0.8785 | 39.3 |
PiCANet [46] | 0.0449 | 0.8490 | 0.9317 | 0.8337 | 49.1 |
Ours | 0.0202 | 0.9007 | 0.9654 | 0.8860 | 47.6 |
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Sun, Y.; Geng, S.; Zheng, C.; Xu, C.; Guo, H.; Feng, Y. DEPANet: A Differentiable Edge-Guided Pyramid Aggregation Network for Strip Steel Surface Defect Segmentation. Algorithms 2025, 18, 279. https://doi.org/10.3390/a18050279
Sun Y, Geng S, Zheng C, Xu C, Guo H, Feng Y. DEPANet: A Differentiable Edge-Guided Pyramid Aggregation Network for Strip Steel Surface Defect Segmentation. Algorithms. 2025; 18(5):279. https://doi.org/10.3390/a18050279
Chicago/Turabian StyleSun, Yange, Siyu Geng, Chengyi Zheng, Chenglong Xu, Huaping Guo, and Yan Feng. 2025. "DEPANet: A Differentiable Edge-Guided Pyramid Aggregation Network for Strip Steel Surface Defect Segmentation" Algorithms 18, no. 5: 279. https://doi.org/10.3390/a18050279
APA StyleSun, Y., Geng, S., Zheng, C., Xu, C., Guo, H., & Feng, Y. (2025). DEPANet: A Differentiable Edge-Guided Pyramid Aggregation Network for Strip Steel Surface Defect Segmentation. Algorithms, 18(5), 279. https://doi.org/10.3390/a18050279