DAHD-YOLO: A New High Robustness and Real-Time Method for Smoking Detection

Recent advancements in AI technologies have driven the extensive adoption of deep learning architectures for recognizing human behavioral patterns. However, the existing smoking behavior detection models based on object detection still have problems, including poor accuracy and insufficient real-time performance. Especially in complex environments, the existing models often struggle with erroneous detections and missed detections. In this paper, we introduce DAHD-YOLO, a model built upon the foundation of YOLOv8. We first designed the DBCA module to replace the bottleneck component in the backbone. The architecture integrates a diverse branch block and a contextual anchor mechanism, effectively improving the backbone network's ability to extract features. Subsequently, at the end of the backbone, we introduce adaptive fine-grained channel attention (AFGCA) to effectively facilitate the fusion of both overarching patterns and localized details. We introduce the ECA-FPN, an improved version of the feature pyramid network, designed to refine the extraction of hierarchical information and enhance cross-scale feature interactions. The decoupled detection head is also updated via the reparameterization approach. The wise-powerful intersection over union (Wise-PIoU) is adopted as the new bounding box regression loss function, resulting in quicker convergence speed and improved detection outcomes. Our system achieves superior results compared to existing models using a self-constructed smoking detection dataset, reducing computational complexity by 23.20% while trimming the model parameters by 33.95%. Moreover, the mAP50 of our model has increased by 5.1% compared to the benchmark model, reaching 86.0%. Finally, we deploy the improved model on the RK3588. After optimizations such as quantization and multi-threading, the system achieves a detection rate of 50.2 fps, addressing practical application demands and facilitating the precise and instantaneous identification of smoking activities.