A General Image Super-Resolution Reconstruction Technique for Walnut Object Detection Model
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Process
2.2. Study Area and Data Set
2.3. Super-Resolution Reconstruction Module–Walnut-SR
2.3.1. Multilevel Depth Adaptive Attention Residual Block
2.3.2. Residual in Residual Dense Block
2.3.3. Convolutional Block Attention Module
2.4. Integration of the Walnut-SR Module into the Object Detection Model
3. Experimental Results
3.1. Experimental Setup
3.2. Evaluation Indicators
3.3. Experimental Results
3.3.1. Comparison Experiments of Walnut-SR Module Integration
3.3.2. Comparison Experiments of Super-Resolution Networks
3.4. Ablation Study
4. Discussion
4.1. Comparison of Object Detection Model Performance before and after Integrating the Walnut-SR Module
4.2. Analysis of Super-Resolution Reconstruction Effects under Different Lighting Conditions
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Label Name | Images Number (Pieces) | Target Number (Pieces) |
---|---|---|
Walnut | 2490 | 12,138 |
Downsample Setting | Model | Scale | Walnut-SR | P | R | mAP50 | mAP50:95 | Parameters (M) |
---|---|---|---|---|---|---|---|---|
down 2 | YOLOv3 | - | - | 0.928 | 0.96 | 0.935 | 0.651 | 61.50 |
√ | 0.950 | 0.96 | 0.943 | 0.694 | 89.23 | |||
YOLOv3-spp | - | - | 0.921 | 0.96 | 0.935 | 0.657 | 62.55 | |
√ | 0.948 | 0.96 | 0.945 | 0.699 | 90.28 | |||
YOLOv3-tiny | - | - | 0.944 | 0.96 | 0.925 | 0.597 | 8.67 | |
√ | 0.963 | 0.97 | 0.928 | 0.638 | 36.40 | |||
YOLOv5 | n | - | 0.916 | 0.98 | 0.944 | 0.658 | 1.77 | |
s | 0.930 | 0.98 | 0.943 | 0.666 | 7.02 | |||
m | 0.923 | 0.97 | 0.944 | 0.666 | 20.87 | |||
l | 0.928 | 0.96 | 0.936 | 0.668 | 46.14 | |||
x | 0.925 | 0.97 | 0.946 | 0.664 | 86.22 | |||
n | √ | 0.937 | 0.98 | 0.951 | 0.683 | 29.50 | ||
s | 0.947 | 0.97 | 0.944 | 0.692 | 34.75 | |||
m | 0.948 | 0.97 | 0.949 | 0.705 | 48.60 | |||
l | 0.940 | 0.97 | 0.944 | 0.698 | 73.87 | |||
x | 0.960 | 0.97 | 0.949 | 0.700 | 113.95 | |||
YOLOv6 | n | - | 0.872 | 0.97 | 0.933 | 0.659 | 4.24 | |
s | 0.887 | 0.95 | 0.911 | 0.635 | 16.31 | |||
m | 0.886 | 0.97 | 0.931 | 0.663 | 52.00 | |||
l | 0.879 | 0.95 | 0.913 | 0.652 | 110.90 | |||
x | 0.893 | 0.97 | 0.928 | 0.658 | 173.02 | |||
n | √ | 0.899 | 0.97 | 0.947 | 0.706 | 31.97 | ||
s | 0.908 | 0.97 | 0.941 | 0.694 | 44.04 | |||
m | 0.889 | 0.97 | 0.947 | 0.701 | 79.73 | |||
l | 0.891 | 0.96 | 0.935 | 0.693 | 138.63 | |||
x | 0.906 | 0.97 | 0.943 | 0.701 | 200.75 | |||
YOLOv7 | - | - | 0.917 | 0.99 | 0.943 | 0.653 | 37.20 | |
√ | 0.944 | 0.98 | 0.945 | 0.692 | 64.93 | |||
YOLOv7-tiny | - | - | 0.985 | 0.99 | 0.910 | 0.576 | 6.01 | |
√ | 0.992 | 0.98 | 0.917 | 0.609 | 33.74 | |||
YOLOv8 | n | - | 0.868 | 0.97 | 0.933 | 0.661 | 3.01 | |
s | 0.871 | 0.97 | 0.936 | 0.666 | 11.14 | |||
m | 0.883 | 0.97 | 0.931 | 0.661 | 25.86 | |||
l | 0.874 | 0.97 | 0.926 | 0.654 | 43.63 | |||
x | 0.873 | 0.97 | 0.932 | 0.661 | 68.15 | |||
n | √ | 0.890 | 0.97 | 0.943 | 0.701 | 30.74 | ||
s | 0.891 | 0.98 | 0.947 | 0.709 | 38.87 | |||
m | 0.898 | 0.97 | 0.944 | 0.702 | 53.59 | |||
l | 0.907 | 0.97 | 0.938 | 0.697 | 71.36 | |||
x | 0.903 | 0.98 | 0.945 | 0.707 | 95.88 | |||
YOLOv9 | t | - | 0.871 | 0.98 | 0.949 | 0.661 | 3.72 | |
s | 0.876 | 0.97 | 0.953 | 0.687 | 9.80 | |||
m | 0.866 | 0.98 | 0.956 | 0.684 | 32.88 | |||
c | 0.884 | 0.96 | 0.952 | 0.700 | 51.18 | |||
t | √ | 0.898 | 0.98 | 0.953 | 0.707 | 31.45 | ||
s | 0.897 | 0.98 | 0.955 | 0.722 | 37.53 | |||
m | 0.904 | 0.98 | 0.955 | 0.722 | 60.61 | |||
c | 0.905 | 0.97 | 0.958 | 0.731 | 78.91 | |||
w-YOLO | t0 | - | 0.853 | 0.97 | 0.925 | 0.644 | 5.87 | |
t1 | 0.856 | 0.96 | 0.922 | 0.618 | 9.51 | |||
t0 | √ | 0.907 | 0.98 | 0.943 | 0.698 | 33.60 | ||
t1 | 0.888 | 0.98 | 0.946 | 0.701 | 37.24 |
Downsample Setting | Model | Scale | Walnut-SR | P | R | mAP50 | mAP50:95 | Parameters (M) |
---|---|---|---|---|---|---|---|---|
down 4 | YOLOv3 | - | - | 0.910 | 0.82 | 0.742 | 0.407 | 61.50 |
√ | 0.952 | 0.93 | 0.895 | 0.612 | 89.23 | |||
YOLOv3-spp | - | - | 0.924 | 0.84 | 0.707 | 0.404 | 62.55 | |
√ | 0.947 | 0.93 | 0.894 | 0.608 | 90.28 | |||
YOLOv3-tiny | - | - | 0.890 | 0.88 | 0.734 | 0.397 | 8.67 | |
√ | 0.957 | 0.94 | 0.882 | 0.573 | 36.40 | |||
YOLOv5 | n | - | 0.915 | 0.89 | 0.764 | 0.454 | 1.77 | |
s | 0.928 | 0.87 | 0.754 | 0.425 | 7.02 | |||
m | 0.929 | 0.88 | 0.778 | 0.457 | 20.87 | |||
l | 0.929 | 0.87 | 0.790 | 0.452 | 46.14 | |||
x | 0.914 | 0.87 | 0.785 | 0.449 | 86.22 | |||
n | √ | 0.926 | 0.96 | 0.898 | 0.599 | 29.50 | ||
s | 0.957 | 0.96 | 0.906 | 0.621 | 34.75 | |||
m | 0.942 | 0.93 | 0.896 | 0.616 | 48.60 | |||
l | 0.940 | 0.94 | 0.901 | 0.618 | 73.87 | |||
x | 0.946 | 0.94 | 0.900 | 0.613 | 113.95 | |||
YOLOv6 | n | - | 0.857 | 0.88 | 0.751 | 0.435 | 4.24 | |
s | 0.857 | 0.78 | 0.610 | 0.342 | 16.31 | |||
m | 0.870 | 0.85 | 0.682 | 0.401 | 52.00 | |||
l | 0.856 | 0.81 | 0.655 | 0.387 | 110.90 | |||
x | 0.855 | 0.87 | 0.692 | 0.392 | 173.02 | |||
n | √ | 0.900 | 0.95 | 0.892 | 0.613 | 31.97 | ||
s | 0.906 | 0.95 | 0.889 | 0.605 | 44.04 | |||
m | 0.887 | 0.95 | 0.884 | 0.606 | 79.73 | |||
l | 0.884 | 0.93 | 0.878 | 0.604 | 138.63 | |||
x | 0.902 | 0.95 | 0.884 | 0.607 | 200.75 | |||
YOLOv7 | - | - | 0.870 | 0.88 | 0.731 | 0.400 | 37.20 | |
√ | 0.936 | 0.97 | 0.892 | 0.600 | 64.93 | |||
YOLOv7-tiny | - | - | 0.927 | 0.96 | 0.729 | 0.388 | 6.01 | |
√ | 0.983 | 0.97 | 0.856 | 0.527 | 33.74 | |||
YOLOv8 | n | - | 0.871 | 0.82 | 0.662 | 0.370 | 3.01 | |
s | 0.889 | 0.93 | 0.753 | 0.445 | 11.14 | |||
m | 0.858 | 0.86 | 0.730 | 0.424 | 25.86 | |||
l | 0.865 | 0.83 | 0.695 | 0.373 | 43.63 | |||
x | 0.864 | 0.86 | 0.728 | 0.420 | 68.15 | |||
n | √ | 0.904 | 0.95 | 0.890 | 0.612 | 30.74 | ||
s | 0.890 | 0.95 | 0.892 | 0.613 | 38.87 | |||
m | 0.897 | 0.96 | 0.897 | 0.618 | 53.59 | |||
l | 0.894 | 0.95 | 0.884 | 0.604 | 71.36 | |||
x | 0.897 | 0.95 | 0.887 | 0.610 | 95.88 | |||
YOLOv9 | t | - | 0.866 | 0.87 | 0.781 | 0.439 | 3.72 | |
s | 0.823 | 0.84 | 0.772 | 0.442 | 9.80 | |||
m | 0.888 | 0.86 | 0.791 | 0.479 | 32.88 | |||
c | 0.868 | 0.76 | 0.764 | 0.497 | 51.18 | |||
t | √ | 0.893 | 0.96 | 0.912 | 0.625 | 31.45 | ||
s | 0.893 | 0.96 | 0.915 | 0.638 | 37.53 | |||
m | 0.896 | 0.95 | 0.916 | 0.637 | 60.61 | |||
c | 0.902 | 0.93 | 0.917 | 0.648 | 78.91 | |||
w-YOLO | t0 | - | 0.833 | 0.96 | 0.768 | 0.443 | 5.87 | |
t1 | 0.805 | 0.9 | 0.751 | 0.396 | 9.51 | |||
t0 | √ | 0.886 | 0.96 | 0.901 | 0.611 | 33.60 | ||
t1 | 0.886 | 0.95 | 0.893 | 0.607 | 37.24 |
Model | Walnut-SR | Downsample Setting | FPS |
---|---|---|---|
w-YOLOt0 | × | - | 50 |
√ | down 2 | 1.18 2.13 | |
down 4 |
Model | Downsample Setting | PSNR (dB) | SSIM | DS-SSIM | NIQE | Parameters (M) | Inference Time (s) |
---|---|---|---|---|---|---|---|
ESRGAN | down 2 | 25.3059 | 0.7821 | 0.9851 | 4.6295 | 16.70 | 0.40 |
SRGAN | 27.5122 | 0.8542 | 0.9911 | 6.7974 | 1.37 | 0.04 | |
EDSR_L | 26.9583 | 0.8382 | 0.9903 | 7.1957 | 40.73 | 0.25 | |
RCAN | 27.1333 | 0.8456 | 0.9908 | 8.3306 | 15.44 | 0.20 | |
SwinIR | 27.1493 | 0.8458 | 0.9907 | 7.2899 | 11.75 | 0.80 | |
Our | 26.6227 | 0.8435 | 0.9828 | 8.0534 | 27.73 | 0.83 | |
ESRGAN | down 4 | 21.3371 | 0.5947 | 0.9394 | 6.9394 | 16.70 | 0.06 |
SRGAN | 21.3525 | 0.5914 | 0.9384 | 6.8006 | 1.52 | 0.01 | |
EDSR_L | 21.1442 | 0.5742 | 0.9350 | 6.6678 | 43.09 | 0.15 | |
RCAN | 21.1961 | 0.5816 | 0.9365 | 6.9260 | 15.44 | 0.06 | |
SwinIR | 21.3868 | 0.5910 | 0.9384 | 6.4596 | 11.90 | 0.15 | |
Our | 21.4266 | 0.5960 | 0.9402 | 6.9663 | 27.73 | 0.39 |
Group | Module | ×2 | ×4 | Number of Parameters | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
MDAARB | RRDB | CBAM | PSNR (dB) | SSIM | DS-SSIM | NIQE | PSNR (dB) | SSIM | DS-SSIM | NIQE | ||
1 | √ | 21.28 | 0.7248 | 0.9351 | 5.7271 | 18.94 | 0.4657 | 0.9395 | 6.8131 | 2,773,3571 | ||
2 | √ | √ | 24.74 | 0.8045 | 0.9896 | 7.3237 | 18.95 | 0.4867 | 0.9163 | 6.6401 | 2,773,3571 | |
3 | √ | √ | √ | 24.66 | 0.8031 | 0.9828 | 8.0548 | 19.26 | 0.4991 | 0.9402 | 6.9663 | 2,773,4182 |
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Wu, M.; Yang, X.; Yun, L.; Yang, C.; Chen, Z.; Xia, Y. A General Image Super-Resolution Reconstruction Technique for Walnut Object Detection Model. Agriculture 2024, 14, 1279. https://doi.org/10.3390/agriculture14081279
Wu M, Yang X, Yun L, Yang C, Chen Z, Xia Y. A General Image Super-Resolution Reconstruction Technique for Walnut Object Detection Model. Agriculture. 2024; 14(8):1279. https://doi.org/10.3390/agriculture14081279
Chicago/Turabian StyleWu, Mingjie, Xuanxi Yang, Lijun Yun, Chenggui Yang, Zaiqing Chen, and Yuelong Xia. 2024. "A General Image Super-Resolution Reconstruction Technique for Walnut Object Detection Model" Agriculture 14, no. 8: 1279. https://doi.org/10.3390/agriculture14081279