Insufficient accuracy in detecting small targets is a common challenge in the field of target detection, and YOLOv12 is targeted to solve this problem by introducing an innovative attention mechanism and structural optimization. The following are the specific solutions:
1. Selection of appropriate model sizes: Prioritize the use of medium to large models (e.g., YOLOv12-M/L), which have stronger feature extraction capabilities. For scenarios with limited computational resources, performance can be balanced by quantization or distillation techniques.
2. Utilizing regional attention mechanisms: YOLOv12's Area Attention module automatically focuses on key areas, implementation steps:
- The function is activated automatically during training, no additional configuration is required
- Increase the input resolution by adjusting the imgsz parameter (recommended ≥ 640)
- Observation of changes in small target AP values during the validation phase
3. Data enhancement strategy::
- Add small target sample proportions to data.yaml
- Using mosaic data enhancement (enabled by default)
- Add RandomZoom Enhancement
4. Loss function tuning: Modify the loss weights in runs/detect/train/args.json to increase the small target weight coefficients.
After implementation, you can work with the Supervision tool to visualize the inspection results and compare the accuracy indicators before and after adjustment.
This answer comes from the articleYOLOv12: Open source tool for real-time image and video target detectionThe
