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A novel algorithm for small object detection based on YOLOv4
Jiangshu Wei1, Gang Liu1, Siqi Liu1
1College of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.
Peerj. Computer Science
|June 22, 2023
Summary
This study enhances small object detection using a modified YOLOv4 network, improving accuracy in complex environments like drone imagery and road scenes. The new model offers better performance with fewer parameters for real-time applications.
Area of Science:
- Computer Vision
- Deep Learning
- Object Detection
Background:
- Small object detection is challenging due to complex backgrounds, noise, and occlusion.
- Traditional methods struggle with accuracy in real-world scenarios like aerial surveys and road monitoring.
Purpose of the Study:
- To develop an improved small object detection network based on YOLOv4.
- To enhance accuracy and efficiency for detecting small objects in complex environments.
Main Methods:
- Incorporated Cross-Stage Partial Network (CSPNet) into the spatial pyramid pool (SPP) structure.
- Introduced a dedicated small object detection head and a shallow feature extraction branch.
- Integrated a weighting mechanism for feature fusion and a coordinated attention (CA) module.
Main Results:
- Achieved 52.76% mAP on a drone aerial dataset, outperforming YOLOv4 and YOLOv5L.
- Reached 96.98% accuracy on a road traffic light dataset, surpassing existing models.
- Demonstrated real-time detection speed with only 44M parameters.
Conclusions:
- The proposed YOLOv4-based network significantly improves small object detection accuracy in complex scenes.
- The model offers an efficient and effective solution for applications like drone surveillance and autonomous driving.
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