Human figure detection in Han portrait stone images via enhanced YOLO-v5

Abstract The unearthed Han Dynasty portrait stones are an important part of China’s ancient artistic heritage, and detecting human images in these stones is a critical prerequisite for studying their artistic value. However, high-precision target detection techniques often result in a large number of parameters, making them unsuitable for portable devices. In this work, we propose a new human image target detection model based on an enhanced YOLO-v5. We discovered that the complex backgrounds, dense group targets, and significant scale variations of targets within large scenes in portrait stones present significant challenges for human target image detection. Therefore, we first incorporated the SPD-Conv convolution and Coordinate Attention self-attention mechanism modules into the YOLO-v5 architecture, aiming to enhance the model’s recognition precision for small target images within Han portrait stones and strengthen its resistance to background disturbances. Moreover, we introduce DIoU NMS and Alpha-IoU Loss to improve the detector’s performance in dense target scenarios, reducing the omission of densely packed objects. Finally, the experimental results from our collected dataset of Han Dynasty stone figure images demonstrate that our method achieves fast convergence and high recognition accuracy. This approach can be better applied to the target detection tasks of special character images in complex backgrounds.