Research on music symbol recognition model based on YOLOv8s

Optical Music Recognition aims to automatically extract music information such as notes and beats from printed or handwritten music score images using computer vision technology, which holds significant value in fields such as music information retrieval and sheet music digitization, etc. This paper introduces YOLOv8 object detection algorithms into music symbol recognition field, and an improved model named YOLO-Score is proposed based on YOLOv8s.This model brings SPD-Conv into the backbone feature network to enhance the recognition ability for small targets; incorporates LSK selective attention mechanism to focus on more meaningful feature information using extensive contextual information; redesigns the detection layer by adding a small target detection branch and removing the large target detection branch to strengthen the network's feature fusion capability; and employs Shape-IoU as the bounding box regression loss function to improve network convergence accuracy. The experimental results show an 11.2% increase in precision, a 33.0% increase in recall, a 26.6% increase in mAP, and a reduction of 1.8Mb in weight file size compared to the YOLOv8s model.