Dense Small Object Detection Based on an Improved YOLOv7 Model
Abstract
:1. Introduction
2. Related Work
3. Proposed Method
3.1. More Focus on Small Object Detection Head
3.2. Multi-Scale Feature Extraction Module
3.3. SPD Conv Downsampling
3.4. ELAN Model Optimization
4. Experiments and Analysis of Results
4.1. Data Set
4.2. Experimental Settings
4.3. Evaluation Metrics
4.4. Experimental Analysis of Different Improvement Points
4.4.1. Improved Detection Head for YOLOv7
4.4.2. C3R2N Multi-Scale Feature Extraction Module
4.4.3. Introduce SPD Conv Downsampling Module
4.4.4. Adjusting the Number of Channels of the Feature Fusion Module
4.5. Ablation Experiments
4.6. Comparative Experiments
4.7. Visual Analysis
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameters | Value |
---|---|
image size | 640 × 640 |
epochs | 300 |
warmup | 3 |
batch size | 16 |
optimizer | Adam |
learning rate | 0.01 |
momentum | 0.937 |
weight decay | 0.0005 |
Experiment | mAP(s)% | mAP(m)% | mAP(l)% | [email protected]% | Parameters/M |
---|---|---|---|---|---|
1 | 18.6 | 38.7 | 48.8 | 48.8 | 36.53 |
2 | 21.0 | 40.7 | 47.8 | 50.5 | 37.10 |
3 | 21.3 | 40.8 | 47.2 | 50.9 | 15.49 |
4 | 21.6 | 40.7 | 49.0 | 51.4 | 11.31 |
Experiment | [email protected]% | [email protected]:0.95% | Parameters/M |
---|---|---|---|
1 | 52.1 | 30.4 | 10.24 |
2 | 51.8 | 30.4 | 10.22 |
3 | 51.7 | 30.3 | 10.22 |
4 | 51.2 | 29.7 | 10.37 |
5 | 51.9 | 30.4 | 9.30 |
6 | 51.3 | 29.8 | 9.08 |
7 | 50.7 | 29.6 | 9.01 |
Baseline | 51.4 | 30.0 | 11.31 |
Experiment | mAP(s)% | mAP(m)% | mAP(l)% | [email protected]% | Parameters/M |
---|---|---|---|---|---|
1 | 23.0 | 43.3 | 46.8 | 53.8 | 10.10 |
2 | 22.9 | 42.3 | 46.5 | 53.4 | 10.65 |
Baseline | 21.7 | 41.5 | 46.1 | 51.9 | 9.30 |
Experiment | mAP(s)% | mAP(m)% | mAP(l)% | [email protected]% | Parameters/M |
---|---|---|---|---|---|
Baseline | 23.0 | 43.3 | 46.8 | 53.8 | 10.10 |
1 | 24.2 | 43.3 | 49.6 | 55.1 | 10.89 |
Method | [email protected]% | [email protected]:0.95% | Parameters/M | |
---|---|---|---|---|
A | YOLOv7 | 48.8 | 27.6 | 36.53 |
B | A+P2 | 50.5 | 29.9 | 37.10 |
C | B-P5 | 51.4 | 30.0 | 11.31 |
D | C+C3R2N | 51.9 | 30.3 | 9.30 |
E | D+SPD Conv | 53.8 | 31.8 | 10.10 |
F | E+Channel(Ours) | 55.1 | 32.5 | 10.89 |
Method | [email protected]% | [email protected]:0.95% | Parameters/M |
---|---|---|---|
YOLOv5s | 33.1 | 17.9 | 7.04 |
YOLOv5x | 39.9 | 23.5 | 86.23 |
YOLOv6s | 37.1 | 22.0 | 16.30 |
YOLOv6l | 41.0 | 25.0 | 51.98 |
YOLOv7m | 48.8 | 27.6 | 36.53 |
YOLOv7x | 50.0 | 28.5 | 70.84 |
YOLOv8s | 39.5 | 23.5 | 11.13 |
YOLOv8x | 45.4 | 27.9 | 68.13 |
YOLOv9s | 41.0 | 24.8 | 9.60 |
YOLOv9e | 47.5 | 29.1 | 57.72 |
YOLOv10s | 39.0 | 23.3 | 8.07 |
YOLOv10x | 45.7 | 28.2 | 31.67 |
Drone-YOLOs | 44.3 | 27.0 | 10.9 |
Drone-YOLOl | 51.3 | 31.9 | 76.2 |
MS-YOLO | 53.1 | 31.3 | 79.7 |
Ours | 55.1 | 32.5 | 10.89 |
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Chen, X.; Deng, L.; Hu, C.; Xie, T.; Wang, C. Dense Small Object Detection Based on an Improved YOLOv7 Model. Appl. Sci. 2024, 14, 7665. https://doi.org/10.3390/app14177665
Chen X, Deng L, Hu C, Xie T, Wang C. Dense Small Object Detection Based on an Improved YOLOv7 Model. Applied Sciences. 2024; 14(17):7665. https://doi.org/10.3390/app14177665
Chicago/Turabian StyleChen, Xun, Linyi Deng, Chao Hu, Tianyi Xie, and Chengqi Wang. 2024. "Dense Small Object Detection Based on an Improved YOLOv7 Model" Applied Sciences 14, no. 17: 7665. https://doi.org/10.3390/app14177665