RDM-YOLO: A Lightweight Multi-Scale Model for Real-Time Behavior Recognition of Fourth Instar Silkworms in Sericulture
Abstract
1. Introduction
2. Materials and Methods
2.1. Overview of the Model Improvements
2.2. Res2Net Module
2.3. Distribution Shifting Convolution Module
2.4. Minimum Point Distance Intersection over Union Module
3. Experimental Setup
3.1. Dataset
3.2. Implementation Details
3.3. Evaluation Metrics
4. Results
4.1. RDM-YOLO Ablation Study
4.2. Comparison Between Baseline YOLOv5s and RDM-YOLO
4.3. Comparative Experiment
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
YOLO | You Only Look Once |
DSConv | Distribution shifting convolution |
MPDIoU | Minimum point distance intersection over union |
AP | Average precision |
mAP | Mean average precision |
CNNs | Convolutional neural networks |
FPS | Frames per second |
IoU | Intersection over union |
TP | True positive |
FP | False positive |
FN | False negative |
Adam | Adaptive moment estimation |
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Loss Function | Model | mAP@0.5 | Convergence Epochs | Overlap Robustness |
---|---|---|---|---|
CIoU | YOLOv5s-CIoU | 96.8% | 150 | Moderate |
DIoU | YOLOv5s-DIoU | 97.1% | 145 | Moderate |
MPDIoU | YOLOv5s-MPDIoU | 98.5% | 128 | High |
Environmental Conditions | Value |
---|---|
Temperature | 25 °C ± 1 °C |
Relative humidity | 80% ± 5% |
Light–dark #cycle | 16:8 |
Diet | Fresh mulberry leaves derived from young shoots (10–15 cm in length) with tender leaves |
Parameter Category | Specific Parameter | Value |
---|---|---|
Software environment | Python version | 3.9 |
PyTorch version | 1.11.0 | |
CUDA version | 11.4 | |
Key dependencies | OpenCV version | 4.7.0 |
NumPy version | 1.23.5 | |
Training hyperparameters | Batch size | 16 |
Training epochs | 150 | |
Optimizer | Adam | |
Initial learning rate | 0.01 | |
Momentum | 0.937 | |
Weight decay | 0.0005 | |
Hardware configuration | CPU | Intel Core i7-9750H |
RAM | 64 GB | |
GPU | NVIDIA GeForce RTX 3080Ti |
Experiment | Model | mAP@0.5 | Parameters | FPS |
---|---|---|---|---|
1 | YOLOv5s | 98.0% | 6.7 M | 135 f·s−1 |
2 | YOLOv5s+Res2Net | 98.1% ↑ | 6.4 M ↓ | 138 f·s−1 ↑ |
3 | YOLOv5s+DSConv | 98.6% ↑ | 5.7 M ↓ | 144 f·s−1 ↑ |
4 | YOLOv5s+MPDIoU | 98.5% ↑ | 6.7 M | 132 f·s−1 ↓ |
5 | RDM-YOLO | 99.0% ↑ | 5.4 M ↓ | 150 f·s−1 ↑ |
Model | Parameters | Precision | mAP@0.5 | mAP@0.5:0.95 | FPS |
---|---|---|---|---|---|
YOLOv3-tiny | 8.7 M | 85.9% | 85.8% | 77.8% | 140 f·s−1 |
YOLOv5s | 6.7 M | 96.3% | 98.0% | 90.1% | 135 f·s−1 |
YOLOv6s | 4.1 M | 95.3% | 98.9% | 91.2% | 105 f·s−1 |
YOLOv7-tiny | 6.1 M | 92.0% | 97.2% | 89.5% | 118 f·s−1 |
YOLOv8n | 2.9 M | 97.6% | 98.9% | 90.9% | 114 f·s−1 |
YOLOv8-ghost | 1.6 M | 95.7% | 97.9% | 88.9% | 120 f·s−1 |
YOLOv9t | 2.6 M | 96.6% | 98.8% | 91.1% | 147 f·s−1 |
YOLOv11s | 9.4 M | 97.5% | 98.3% | 90.6% | 148 f·s−1 |
RDM-YOLO | 5.4 M | 97.7% | 99.0% | 92.1% | 150 f·s−1 |
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Gao, J.; Sun, J.; Wu, X.; Dai, C. RDM-YOLO: A Lightweight Multi-Scale Model for Real-Time Behavior Recognition of Fourth Instar Silkworms in Sericulture. Agriculture 2025, 15, 1450. https://doi.org/10.3390/agriculture15131450
Gao J, Sun J, Wu X, Dai C. RDM-YOLO: A Lightweight Multi-Scale Model for Real-Time Behavior Recognition of Fourth Instar Silkworms in Sericulture. Agriculture. 2025; 15(13):1450. https://doi.org/10.3390/agriculture15131450
Chicago/Turabian StyleGao, Jinye, Jun Sun, Xiaohong Wu, and Chunxia Dai. 2025. "RDM-YOLO: A Lightweight Multi-Scale Model for Real-Time Behavior Recognition of Fourth Instar Silkworms in Sericulture" Agriculture 15, no. 13: 1450. https://doi.org/10.3390/agriculture15131450
APA StyleGao, J., Sun, J., Wu, X., & Dai, C. (2025). RDM-YOLO: A Lightweight Multi-Scale Model for Real-Time Behavior Recognition of Fourth Instar Silkworms in Sericulture. Agriculture, 15(13), 1450. https://doi.org/10.3390/agriculture15131450