LESM-YOLO: An Improved Aircraft Ducts Defect Detection Model.
Runyuan Wen1, Yong Yao1, Zijian Li1
1School of Computer Science and Technology, Xidian University, Xi'an 710126, China.
Sensors (Basel, Switzerland)
|July 13, 2024
Summary
A new defect detection model, LESM-YOLO, improves aircraft duct inspections, especially in low light. This lightweight model enhances accuracy and speed for real-time monitoring, ensuring aircraft safety.
Area of Science:
- Aerospace Engineering
- Computer Vision
- Artificial Intelligence
Background:
- Aircraft ducts are critical for aircraft systems, requiring regular inspection to prevent failures.
- Manual inspection of aircraft ducts is inefficient and costly, particularly in low-light environments.
Purpose of the Study:
- To develop an advanced defect detection model for aircraft ducts that overcomes the limitations of traditional methods.
- To enhance inspection accuracy and efficiency, especially under challenging low-light conditions.
Main Methods:
- Proposed a novel defect detection model, LESM-YOLO, integrating a lighting enhancement module for low-light conditions.
- Employed space-to-depth convolution for a lightweight model suitable for edge devices.
- Introduced Mixed Local Channel Attention (MLCA) to balance model complexity and accuracy.
Main Results:
- LESM-YOLO achieved a mean Average Precision (mAP) of 96.3%, a 5.4% improvement over the original model.
- The model demonstrated a detection speed of 138.7, meeting real-time monitoring requirements.
- Validation confirmed the model's effectiveness in detecting dark defects in aircraft ducts.
Conclusions:
- LESM-YOLO offers a significant advancement in automated defect detection for aircraft ducts.
- The model's performance in low-light conditions and its real-time capabilities provide valuable technical support for aviation safety.
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