YOLOv8-based small-target disinfection region detection model under complex indoor conditions

In response to the challenge presented by the wide range of scenarios and targets in intricate disinfection environments, this study introduces an improved target detection algorithm. The algorithm integrates a Coordinate Attention mechanism, thereby enhancing the ability to capture information related to small targets. Additionally, the SPPFCSPC module effectively enhances the feature extraction capabilities of the backbone feature network. To address issues related to slow convergence and imprecise regression outcomes, the Focal and Efficient IOU loss function is utilized. Experimental findings demonstrate that the proposed algorithm achieves a remarkable mean Average Precision of 91.4%, representing a notable enhancement of 2.4% over the YOLOv8n model.