BS-YOLO:A Multi-scale Object Detection Model for Complex Environments

Helmet detection is a critical component of road safety. However, existing detection algorithms face challenges in accuracy and recall, particularly when dealing with multi-scale objects in complex environments. To address these issues, this study proposes an improved YOLOv8-based model, denot-ed as BS-YOLO. Firstly, drawing inspiration from StarNet, we propose two modules, namely StarFuseBlock and StarSPPF, to enhance the feature extrac-tion capability of the model at shallow layers. Secondly, to mitigate the loss of texture features of small and medium-sized objects during the feature propagation process, we propose ResBiBlock, which captures global features through residual connections and Biformer Attention. Finally, MPDIoU is employed as a superior alternative to CIoU to enhance computational effi-ciency and provide greater robustness in situations where predicted boxes do not align with ground-truth boxes. To validate the performance of the pro-posed model, a series of extensive experiments were performed on the TWHD dataset.Results show that the BS-YOLO yields a 2.2 increase in precision, a 2.8 improvement in recall, and a 2.2 enhancement in mAP compared to the YOLOv8 baseline. The experimental results indicate that the proposed improvements effectively enhance the performance of the baseline model, particularly in terms of robustness when dealing with multi-scale ob-jects in complex scenarios.