A Lightweight Insulator Defect Detection Model Based on Drone Images
Abstract
:1. Introduction
- (1)
- We propose IDD-YOLO, a lightweight and accurate detection model, specifically designed for detecting insulator defects in transmission lines. Compared to the existing mainstream insulator defect detection models, IDD-YOLO demonstrates a higher accuracy and a smaller number of parameters. Additionally, we constructed ID-2024, a dataset that includes multiple types of insulator defects, to better meet practical inspection needs.
- (2)
- We propose LCSA, a novel attention mechanism, and integrate it with the GhostNet backbone network, enabling the model to extract features more comprehensively without increasing computational parameters.
- (3)
- We incorporate the GSConv and C3Ghost modules into the neck network to reduce the model size. Additionally, the EIOU loss function and Mish activation function are utilized to optimize detection speed and accuracy.
- (4)
- We use TensorRT to compress and accelerate the IDD-YOLO model and successfully deploy it on the embedded device Jetson TX2 NX to verify the model’s feasibility in practical application scenarios.
2. Related Work
3. Methods
3.1. IDD-YOLO Object Detection Algorithm
3.2. LCSA Attention Mechanism
3.3. GSConv Module
3.4. C3Ghost Module
3.5. EIOU Loss Function
3.6. Mish Activation Function
4. Experiments
4.1. Dataset
4.2. Experimental Setup
4.3. Evaluation Metrics
4.4. Comparative Experiments with Mainstream Attention Mechanisms
4.5. Comparison Experiment with Mainstream Lightweight Object Detection Algorithms
4.6. Experimentation on the SFID Dataset
4.7. Acceleration and Deployment on Edge Platforms
4.8. Ablation Study
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Models | Detection Accuracy | Advantages | Disadvantages |
---|---|---|---|
[7] | Achieved a precision of 85.95% on a self-built insulator dataset | Strong quantitative validation | Complex implementation |
[8] | Achieved a recall rate of 98.97% on a self-built dataset | High detection accuracy | Sensitivity to initial conditions |
[9] | Achieved a maximum recall rate of 98% on a self-built dataset | High performance metrics | Manual ground truth creation |
[15] | Achieved a 97.6% average precision (AP) on a self-built test set of 112 images | Efficient use of pretrained models | Slow inference speed |
[16] | Achieved 92.5% accuracy on a self-built dataset | High model accuracy | Complex real-world deployment, high computational resource demand |
[17] | Achieved a mean Average Precision (mAP) of 90.8% on a self-built dataset | High detection accuracy | Requires significant computational resources, risk of overfitting |
[27] | Achieved a mAP of 95.63% on the CPLMID dataset | High accuracy and speed | Complex model structure, difficult to deploy in practice |
[28] | Achieved an AP of 62.48% on a self-built dataset | Efficient on edge devices, high detection speed | Highly dependent on data quality, slightly lower detection accuracy |
[29] | Achieved a mAP of 90.1% on the WI dataset | High model accuracy | Complex real-world deployment |
[30] | Achieved a mAP of 97.1% on the UPID dataset | High detection accuracy | Poor real-time applicability |
[31] | Achieved a mAP of 85% on a self-built dataset | Good robustness | Complex operations, high computational demands |
[32] | Achieved a 96.2% F1-score on the SFID dataset | Enhanced dataset and open-source, provides benchmark models | Slow real-time inference speed |
[33] | Achieved a mAP of 89.1% on the DVID dataset | High detection accuracy | Difficult real-world deployment |
[34] | Achieved a mAP of 99.4% on a self-built dataset | High accuracy and recall | Complexity and high computational demand |
[35] | Achieved a precision of 97.38% on the ID dataset | Focus on practical application | Deployment requires substantial computational resources |
[36] | Achieved a mAP of 94.2% on a self-built dataset | High accuracy in detecting small targets | Long training and inference times |
[37] | Achieved a precision of 90.71% on a self-built dataset | Robust algorithm performance | High computational load |
[38] | Achieved a mAP of 85.6% on a dataset collected online | Lightweight model | Dependent on high-quality data |
[39] | Achieved a mAP of 91.34% on a self-built dataset | Strong real-time detection capabilities | High computational resource needs |
[40] | Achieved a mAP of 65.1% on the IDID-Plus dataset | Lightweight model, efficient on edge devices | Risk of overfitting |
[41] | Achieved a mAP of 94.24% on the RSIn-Dataset | High detection accuracy | Difficult real-world deployment |
Classes | Number |
---|---|
Flashover | 5078 |
Broken | 1862 |
Insulator | 5445 |
Missing cap | 563 |
Models | [email protected] | Param. (M) | R | [email protected]:0.95 | P | FLOPs (G) |
---|---|---|---|---|---|---|
+CA [45] | 64.3% | 2,841,770 | 59.5% | 37.9% | 77.5% | 4.8 |
+CBAM [46] | 62.4% | 2,918,347 | 61.9% | 35.3% | 74.2% | 5.0 |
+ECA [48] | 59% | 2,570,988 | 57.5% | 33.7% | 71.5% | 4.5 |
+SE [47] | 61.9% | 2,582,196 | 62.3% | 35.1% | 71.6% | 4.5 |
+LCSA | 66.2% | 2,851,872 | 63.6% | 38.5% | 74.9% | 5.1 |
Models | [email protected] | Param. (M) | R | [email protected]:0.95 | P | FLOPs (G) |
---|---|---|---|---|---|---|
BC-YOLO [33] | 64.4% | 7,247,858 | 61.4% | 37.3% | 73.1% | 16.5 |
I-YOLOv5 [34] | 61.4% | 3,803,684 | 58.1% | 34.9% | 71.1% | 9.7 |
GC-YOLO [36] | 54.4% | 9,645,420 | 53.2% | 31.8% | 63.7% | 29.0 |
YOLOv5s [22] | 62.1% | 7,020,913 | 60.3% | 35.4% | 72.7% | 15.8 |
YOLOv6n [24] | 56.3% | 4,234,140 | 53.9% | 34.7% | 70.2% | 11.8 |
YOLOv7t [25] | 65.9% | 6,023,106 | 60.3% | 38.6% | 78.7% | 13.2 |
YOLOv8n [26] | 59.4% | 3,022,812 | 58.7% | 37.3% | 73.7% | 8.1 |
YOLOv8s [26] | 62.5% | 11,127,132 | 60.3% | 39.5% | 74.3% | 28.4 |
IDD-YOLO | 66.2% | 2,851,872 | 63.6% | 38.5% | 74.9% | 5.1 |
Model | [email protected] | [email protected]:0.95 |
---|---|---|
Mask R-CNN [13] | 98.3% | 82.0% |
Faster R-CNN [11] | 98.4% | 80.1% |
YOLOX [23] | 99.4% | 86.0% |
Swin-Transformer [58] | 99.0% | 86.4% |
YOLOv5s [22] | 99.3% | 87.0% |
IDD-YOLO | 99.4% | 87.2% |
Model | Param. (M) | FPS |
---|---|---|
BC-YOLO [33] | 7,247,858 | 13.53 |
I-YOLOv5 [34] | 3,803,684 | 12.23 |
GC-YOLO [36] | 9,645,420 | 6.73 |
YOLOv4-tiny [21] | 5,883,356 | 7.28 |
YOLOv5s [22] | 7,020,913 | 15.67 |
YOLOv8s [26] | 11,127,132 | 16.82 |
IDD-YOLO | 2,851,872 | 20.83 |
G-LCSA | G-PANet | EIOU&Mish | [email protected] | Param. (M) | R | [email protected]:0.95 | P | FLOPs (G) |
---|---|---|---|---|---|---|---|---|
- | - | - | 62.1% | 7,020,913 | 60.3% | 35.4% | 72.7% | 15.8 |
√ | - | - | 63.6% | 4,255,120 | 62.3% | 36.6% | 75.5% | 7.6 |
- | √ | - | 64.6% | 5,617,665 | 62.6% | 38% | 78.1% | 13.3 |
- | - | √ | 60.5% | 7,020,913 | 60.8% | 35.9% | 71.8% | 15.8 |
√ | √ | - | 64.7% | 2,851,872 | 65% | 36.8% | 69.5% | 5.1 |
√ | - | √ | 65.2% | 4,255,120 | 63.7% | 37% | 75.8% | 7.6 |
- | √ | √ | 65.6% | 5,617,665 | 62.5% | 37.9% | 76.7% | 13.3 |
√ | √ | √ | 66.2% | 2,851,872 | 63.6% | 38.5% | 74.9% | 5.1 |
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Share and Cite
Lu, Y.; Li, D.; Li, D.; Li, X.; Gao, Q.; Yu, X. A Lightweight Insulator Defect Detection Model Based on Drone Images. Drones 2024, 8, 431. https://doi.org/10.3390/drones8090431
Lu Y, Li D, Li D, Li X, Gao Q, Yu X. A Lightweight Insulator Defect Detection Model Based on Drone Images. Drones. 2024; 8(9):431. https://doi.org/10.3390/drones8090431
Chicago/Turabian StyleLu, Yang, Dahua Li, Dong Li, Xuan Li, Qiang Gao, and Xiao Yu. 2024. "A Lightweight Insulator Defect Detection Model Based on Drone Images" Drones 8, no. 9: 431. https://doi.org/10.3390/drones8090431