Detection and Segmentation of Mouth Region in Stereo Stream Using YOLOv6 and DeepLab v3+ Models for Computer-Aided Speech Diagnosis in Children
Abstract
:1. Introduction
2. Materials
2.1. Data Acquisition Equipment
2.2. Image Dataset Management
- First, we took a portion of 665 images from 16 speakers (8 girls/8 boys; mean age of yrs.) for testing purposes (subset C).
- Another subset (B) consisting of 1092 frames from 25 speakers (10 girls/15 boys; yrs.) was extracted for manual expert delineations for fine-tuning the segmentation model (note Section 3.3.2).
- The remaining subset A of 16,156 frames from 35 children (18 girls/17 boys; yrs.) constituted a base training and validation collection for our processing pipeline.
2.3. Expert Delineations
3. Methods
3.1. Preprocessing and Data Preparation for Training
3.2. Object Detection
3.3. Image Segmentation
3.3.1. Preparation of Weak Labels Using Level Set Segmentation
Lips
Teeth
Tongue
3.3.2. Deeplab v3+ Model
4. Experiments and Results
4.1. Object Detection
4.2. Image Segmentation
4.3. Time Consumption and Processing Speed
5. Discussion
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
SLP | Speech and language pathologist |
CASD | Computer-aided speech diagnosis |
CAD | Computer-aided diagnosis |
ROI | Region of interest |
CLAHE | Contrast-limited adaptive histogram equalization |
DRLSE | Distance-regularized level set evolution |
CNN | Convolutional neural network |
YOLO | You only look once |
DLv3+ | DeepLabv3+ |
IoU | Intersection-over-union |
AP | Average precision |
PvR | Precision vs. recall curve |
DSC | Dice similarity coefficient |
Acc | Accuracy |
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Total Frames | Children | Frames per Child Median (Min–Max) | Lips | Teeth | Tongue | |
---|---|---|---|---|---|---|
Subset A | 16,156 | 35 | 407 (290–880) | 16,079 | 10,738 | 6766 |
Subset B | 1092 | 25 | 45 (29–47) | 1089 | 525 | 289 |
Subset C | 665 | 16 | 45 (16–54) | 664 | 421 | 212 |
Total | 17,913 | 76 | 17,832 | 11,684 | 7267 | |
(99.5%) | (65.2%) | (40.6%) |
Lips | Teeth | Tongue | Overall | ||
---|---|---|---|---|---|
0.50 | AP | 0.999 | 0.745 | 0.651 | 0.798 |
F1 | 0.999 | 0.794 | 0.664 | 0.798 | |
0.75 | AP | 0.999 | 0.748 | 0.661 | 0.803 |
F1 | 0.999 | 0.788 | 0.659 | 0.798 |
Lips | Teeth | Tongue | Overall | ||
---|---|---|---|---|---|
AP | YOLOv3 | 0.999 | 0.612 | 0.276 | 0.629 |
YOLOv5 | 0.999 | 0.774 | 0.532 | 0.768 | |
YOLOv6 | 0.999 | 0.745 | 0.651 | 0.798 | |
YOLOv7 | 1.000 | 0.744 | 0.624 | 0.789 | |
F1 | YOLOv3 | 0.999 | 0.645 | 0.336 | 0.641 |
YOLOv5 | 0.999 | 0.749 | 0.543 | 0.753 | |
YOLOv6 | 0.999 | 0.794 | 0.664 | 0.798 | |
YOLOv7 | 0.999 | 0.822 | 0.605 | 0.800 |
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Sage, A.; Badura, P. Detection and Segmentation of Mouth Region in Stereo Stream Using YOLOv6 and DeepLab v3+ Models for Computer-Aided Speech Diagnosis in Children. Appl. Sci. 2024, 14, 7146. https://doi.org/10.3390/app14167146
Sage A, Badura P. Detection and Segmentation of Mouth Region in Stereo Stream Using YOLOv6 and DeepLab v3+ Models for Computer-Aided Speech Diagnosis in Children. Applied Sciences. 2024; 14(16):7146. https://doi.org/10.3390/app14167146
Chicago/Turabian StyleSage, Agata, and Pawel Badura. 2024. "Detection and Segmentation of Mouth Region in Stereo Stream Using YOLOv6 and DeepLab v3+ Models for Computer-Aided Speech Diagnosis in Children" Applied Sciences 14, no. 16: 7146. https://doi.org/10.3390/app14167146