Unordered Stacked Pillbox Detection Algorithm Based on Improved YOLOv8

To enable fully automated medicine warehousing in intelligent pharmacy systems, accurately detecting disordered, stacked pillboxes is essential. This paper proposes a high-precision detection algorithm for such scenarios based on an improved YOLOv8 framework. The proposed method integrates a novel convolutional module that replaces traditional stride convolutions and pooling layers, enhancing the detection of small, low-resolution targets in computer vision tasks. To further enhance detection accuracy, the Bi-Level Routing Attention (BiFormer) Vision Transformer is incorporated as a Cognitive Computing module. Additionally, the circular Smooth Label (CSL) technique is employed to mitigate boundary discontinuities and periodic anomalies in angle prediction, which often arise in the detection of rotated objects. The experimental results demonstrate that the proposed method achieves a precision of 94.24%, a recall of 90.39%, and a mean average precision (mAP) of 94.16%—improvements of 3.34%, 2.53%, and 3.35%, respectively, over the baseline YOLOv8 model. Moreover, the enhanced detection model outperforms existing rotated-object detection methods while maintaining real-time inference speed. To facilitate reproducibility and future benchmarking, the full dataset and source code used in this study have been released publicly. Although no standardized benchmark currently exists for pillbox detection, our self-constructed dataset reflects key industrial variations in pillbox size, orientation, and stacking, thereby providing a foundation for future cross-domain validation.