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Multi-Object Detection Method in Construction Machinery Swarm Operations Based on the Improved YOLOv4 Model.
Liang Hou1, Chunhua Chen1, Shaojie Wang1,2
1Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361102, China.
Sensors (Basel, Switzerland)
|October 14, 2022
Summary
This study introduces an improved YOLOv4 model for construction machinery detection, enhancing accuracy in complex swarm operations. The method achieves high mean average precision (mAP) while maintaining real-time performance.
Area of Science:
- Computer Vision
- Robotics
- Artificial Intelligence
Background:
- Dense detection, overlapping, and occlusions in construction machinery swarm operations lead to low detection accuracy and missed detections.
- Effective environment perception is crucial for the unmanned and intelligent development of construction machinery.
Purpose of the Study:
- To propose an improved YOLOv4 model for accurate multi-object detection of construction machinery in complex scenarios.
- To enhance the detection performance and address limitations of existing methods in construction machinery swarm operations.
Main Methods:
- Utilized K-means algorithm for anchor box initialization to improve feature learning.
- Replaced pooling operations with dilated convolution to preserve feature map resolution.
- Introduced focus loss to optimize the YOLOv4 loss function and mitigate sample imbalance.
Main Results:
- Achieved a mean average precision (mAP) of 97.03% for multi-object detection of construction machinery.
- The improved model showed a 2.16% increase in mAP compared to the original YOLOv4.
- Maintained a detection speed of 31.11 fps, meeting real-time requirements with a minimal performance decrease.
Conclusions:
- The proposed improved YOLOv4 model effectively enhances multi-object detection accuracy for construction machinery.
- This research provides a foundation for environment perception in construction machinery swarm operations, promoting intelligent development.
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