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DAHD-YOLO: A New High Robustness and Real-Time Method for Smoking Detection
Jianfei Zhang1, Chengwei Jiang1
1School of Computer and Control Engineering, Qiqihar University, Qiqihar 161006, China.
Sensors (Basel, Switzerland)
|March 17, 2025
Summary
This study introduces DAHD-YOLO, an improved deep learning model for accurate and real-time smoking behavior detection. The model enhances feature extraction and fusion, achieving superior performance in complex environments.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Machine Learning
Background:
- Deep learning models are increasingly used for human behavior recognition.
- Existing object detection models for smoking behavior suffer from low accuracy and poor real-time performance, especially in complex settings.
Purpose of the Study:
- To develop an advanced deep learning model for precise and real-time smoking behavior detection.
- To address the limitations of existing models in terms of accuracy and efficiency.
Main Methods:
- Introduced DAHD-YOLO, a novel model based on YOLOv8.
- Incorporated the DBCA module for enhanced feature extraction and adaptive fine-grained channel attention (AFGCA) for better feature fusion.
- Utilized an improved feature pyramid network (ECA-FPN) and a decoupled detection head with Wise-PIoU loss for bounding box regression.
Main Results:
- Achieved superior results on a self-constructed smoking detection dataset compared to existing models.
- Reduced computational complexity by 23.20% and model parameters by 33.95%.
- Increased mAP50 by 5.1% to 86.0% and achieved 50.2 fps detection rate on RK3588 after optimizations.
Conclusions:
- DAHD-YOLO significantly improves smoking behavior detection accuracy and real-time performance.
- The model's efficiency and effectiveness make it suitable for practical applications requiring precise and instantaneous identification of smoking activities.
