ISLS: An Illumination-Aware Sauce-Packet Leakage Segmentation Method
Abstract
:1. Introduction
- (1)
- To alleviate blurred image issues caused by uneven illumination, we propose the ULIE method via an illumination-aware mechanism to enhance the texture details of leakage within the ROI.
- (2)
- The MFFM is proposed to fuse multi-level features with a small number of parameters, capturing multi-scale features to effectively relieve the issue of missing information.
- (3)
- To alleviate the interference of invalid information, we introduce the SSAM by combining spatial and channel attention mechanisms to enhance the discriminability of valid features in the ROI.
- (4)
- We generate a sauce-packet dataset to facilitate research. Furthermore, our method, Mean Intersection over Union (mIoU), achieves 80.8% and Mean Pixel Accuracy (mPA) reaches 90.1% on the self-built dataset, which are +0.9% and +0.9% higher than the previous CNN method [23].
2. Materials and Methods
2.1. Overall Architecture
2.2. Uneven-Light Image Enhancement for Illumination-Aware Region Enhancement
U = −0.169R − 0.331G + 0.5B + 128
V = 0.5R − 0.419G − 0.081B + 128
2.3. ISLS Network Details for Leakage Segmentation
3. Experiments and Results
3.1. Dataset and Experiment Setting
3.2. Evaluation Indexes
3.3. YOLO-Faststv2 Training
3.4. Experiment Analysis of ULIE
3.5. Analysis of Ablation Study
3.6. Comparison with Other Segmentation Methods
3.7. Generalization Performance Validation
3.8. Discussion
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Methods | UEIE | FPN [40] | AF-FPN [41] | MFFM | mIoU (%) | mPA (%) | F1-Score (%) | Params (M) | GFLOPS | FPS |
---|---|---|---|---|---|---|---|---|---|---|
Baseline | 75.6 | 85.6 | 85.2 | 11.333 | 36.671 | 71.2 | ||||
With MobileViTv2 [45] | 73.9 | 84.3 | 83.9 | 11.278 | 36.879 | 63.2 | ||||
With MobileNetv3 [46] | 75.0 | 84.8 | 85.1 | 11.091 | 36.488 | 71.3 | ||||
With MobileOneS4 [47] | 75.3 | 83.6 | 84.9 | 22.951 | 40.312 | 36.2 | ||||
With Regnetx [48] | 75.1 | 86.2 | 84.2 | 12.471 | 36.674 | 70.8 | ||||
With EfficientNetv2s [49] | 76.3 | 85.9 | 86.8 | 29.872 | 40.113 | 42.8 | ||||
+ULIE | ✓ | 77.4 | 89.6 | 86.5 | 11.333 | 36.671 | 71.2 | |||
+R2RNet [21] | 64.3 | 76.1 | 78.0 | 32.362 | 3007.287 | 0.6 | ||||
+FPN | ✓ | 77.3 | 85.2 | 86.3 | 18.906 | 1692.381 | 14.0 | |||
+AF-FPN | ✓ | 75.3 | 83.6 | 84.8 | 18.908 | 1692.737 | 13.2 | |||
+MFFM | ✓ | 78.7 | 87.9 | 87.3 | 11.564 | 66.736 | 48.8 | |||
+ULIE +FPN | ✓ | ✓ | 78.5 | 86.7 | 87.2 | 11.906 | 1692.381 | 14.0 | ||
+ULIE +AF-FPN | ✓ | ✓ | 75.9 | 84.3 | 85.3 | 11.908 | 1692.737 | 13.2 | ||
+ULIE +MFFM | ✓ | ✓ | 79.2 | 89.1 | 87.7 | 11.564 | 66.736 | 48.8 |
Methods | GAM [50] | SimAM [51] | SSAM [52] | mIoU (%) | mPA (%) | F1-Score (%) | Params (M) | GFLOPS | FPS |
---|---|---|---|---|---|---|---|---|---|
BUM | 79.2 | 89.1 | 87.7 | 11.564 | 66.736 | 48.8 | |||
+GAM | ✓ | 78.4 | 89.2 | 87.7 | 20.273 | 119.349 | 3.6 | ||
+SimAM | ✓ | 77.1 | 86.6 | 86.2 | 11.564 | 66.736 | 48.8 | ||
+SSAM | ✓ | 80.8 | 90.1 | 88.8 | 12.266 | 69.973 | 35.2 |
Methods | mIoU (%) | mPA (%) | F1-Score (%) | Params (M) |
---|---|---|---|---|
HRNet [13] | 77.7 | 83.0 | 85.5 | 9.637 |
BiseNetv2 [14] | 75.5 | 79.1 | 85.6 | 5.191 |
SegFormer [15] | 76.5 | 80.8 | 85.1 | 3.715 |
PSPNet [55] | 63.4 | 67.6 | 75.4 | 46.707 |
DeepLabv3 [56] | 78.4 | 83.6 | 86.2 | 54.709 |
LIEPNet [23] | 79.9 | 89.2 | 87.5 | 3.271 |
ISLS (Ours) | 80.8 | 90.1 | 88.8 | 12.266 |
Methods | mIoU (%) | mPA (%) | F1-Score (%) |
---|---|---|---|
Template matching [61] | 40.9 | 59.8 | 54.0 |
Canny edge segmentation [57] | 32.5 | 44.5 | 43.6 |
Contour segmentation [58] | 32.5 | 44.5 | 43.6 |
PCA segmentation [59] | 36.6 | 58.6 | 50.4 |
iForest segmentation [60] | 48.0 | 59.1 | 58.8 |
ISLS (ours) | 80.8 | 90.1 | 88.8 |
Methods | mIoU (%) | mPA (%) | F1-Score (%) | AUC (%) | Params (M) |
---|---|---|---|---|---|
HRNet [13] | 78.4 | 86.6 | 88.2 | 91.6 | 9.637 |
BiseNetv2 [14] | 76.9 | 84.1 | 86 | 89.4 | 5.191 |
SegFormer [15] | 79.4 | 86 | 89.3 | 88.8 | 3.715 |
PSPNet [55] | 76.4 | 83.3 | 86.9 | 89.1 | 46.707 |
DeepLabv3 [56] | 75.8 | 85.1 | 85.8 | 85.0 | 54.709 |
LIEPNet [23] | 79.7 | 85.6 | 89.0 | 91.8 | 3.271 |
AVGSC [62] | - | - | - | 91.3 | - |
L-SVM [62] | - | - | - | 92.6 | - |
NL-SVM [62] | - | - | - | 90.4 | - |
Unext [66] | 81.7 | 89.7 | 1.470 | ||
DoubleU-Net [67] | 82.1 | - | 91.1 | - | - |
E-SegNet [68] | 83.4 | - | 85.3 | - | - |
ISLS (Ours) | 89.3 | 94.7 | 94.2 | 93.2 | 12.266 |
Methods | mIoU (%) | mPA (%) | F1-Score (%) | AUC (%) | Params (M) |
---|---|---|---|---|---|
HRNet [13] | 73.0 | 80.0 | 81.5 | 75.9 | 9.637 |
BiseNetv2 [14] | 71.2 | 73.7 | 74.3 | 63.4 | 5.191 |
SegFormer [15] | 65.7 | 70.1 | 71.3 | 66.8 | 3.715 |
PSPNet [55] | 70.0 | 74.6 | 75.2 | 65.3 | 46.707 |
DeepLabv3 [56] | 72.1 | 81.6 | 75.7 | 71.2 | 54.709 |
LIEPNet [23] | 74.4 | 82.5 | 82.8 | 78.9 | 3.271 |
MLC + PEME [63] | - | - | 75.7 | - | - |
CFE [63] | - | - | 85.1 | - | - |
NDD-Net [64] | - | - | - | 88.2 | - |
CCEANN [65] | - | - | 92.0 | - | 167.280 |
PFCNN [69] | - | - | 82.9 | - | 5.000 |
ISLS (Ours) | 75.8 | 85.0 | 85.2 | 88.4 | 12.266 |
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You, S.; Lin, S.; Feng, Y.; Fan, J.; Yan, Z.; Liu, S.; Ji, Y. ISLS: An Illumination-Aware Sauce-Packet Leakage Segmentation Method. Sensors 2024, 24, 3216. https://doi.org/10.3390/s24103216
You S, Lin S, Feng Y, Fan J, Yan Z, Liu S, Ji Y. ISLS: An Illumination-Aware Sauce-Packet Leakage Segmentation Method. Sensors. 2024; 24(10):3216. https://doi.org/10.3390/s24103216
Chicago/Turabian StyleYou, Shuai, Shijun Lin, Yujian Feng, Jianhua Fan, Zhenzheng Yan, Shangdong Liu, and Yimu Ji. 2024. "ISLS: An Illumination-Aware Sauce-Packet Leakage Segmentation Method" Sensors 24, no. 10: 3216. https://doi.org/10.3390/s24103216
APA StyleYou, S., Lin, S., Feng, Y., Fan, J., Yan, Z., Liu, S., & Ji, Y. (2024). ISLS: An Illumination-Aware Sauce-Packet Leakage Segmentation Method. Sensors, 24(10), 3216. https://doi.org/10.3390/s24103216