CAM-YOLO: A Multi-Scale Cultural Element Detection Network for Thangka Images

To fully understand the cultural content of Thangka images and solve the issues of a large number of parameters, low accuracy and slow inference speed of multi-scale cultural element detection model for Thangka images in complex background. In this paper, we propose a multi-scale cultural element detection model for Thangka images based on improved yolov8. Firstly, C2f-StarNB is integrated into the backbone network instead of the C2f module, and element multiplication is used to achieve the reduction of parameter computation and improvement of inference speed without the need to widen the network. Secondly, the convolutional attention and multi-scale feedforward network module (CAMS) is integrated after the neck network to effectively capture the global contextual information and local features of the Thangka image, and improve the multi-scale cultural element detection performance of the Thangka, especially the small cultural element detection performance is improved significantly. Finally, a more focused bounding box loss Focaler-DIoU is proposed to improve the difficult sample detection performance under sample imbalance conditions. Validated on the Thangka detection dataset, the model outperforms other state-of-the-art methods in terms of the number of parameters and detection accuracy, and achieves an accuracy of 95.6 on mAP@50. The experimental results show that our proposed method has competitive performance for multi-scale cultural element detection in Thangka images.