Object Detection of Northern Chinese Rock Art Images Using YOLOv8 with Omni-Dimensional Dynamic Convolution

Northern Chinese rock art images are characterized by abstract structures, significant morphological variation, blurred carving traces, and complex background textures. These factors pose substantial challenges to automatic detection and classification. To address these issues, this study develops an improved object detection model, termed YOLOv8-ODConv, based on the YOLOv8 framework for rock art image recognition. The proposed model integrates Omni-Dimensional Dynamic Convolution (ODConv) into the key layers of the Backbone and Neck. This design enables convolution kernels to adapt dynamically across spatial, channel, and kernel dimensions, thereby enhancing the representation of complex carving structures, fine-grained local differences, and multi-scale features. A dedicated dataset of Northern Chinese rock art images is constructed, including three representative categories: Anthropomorphic_Face, Deer, and Human_Horse. To improve model robustness under challenging visual conditions, multiple data augmentation strategies are applied, including brightness variation, noise perturbation, and geometric transformations. Experimental results demonstrate that YOLOv8-ODConv outperforms SSD, YOLOv5, YOLOv8, and YOLOv8–Backbone–ODConv across multiple evaluation metrics. The proposed model achieves an F1-score of 95.0%, Recall of 89.3%, mAP@0.5 of 98.9%, mAP@0.5–0.95 of 84.6%, and an inference speed of 89.7 FPS. Confusion matrix analysis and visual detection results further confirm that the model effectively distinguishes typical rock art categories and maintains stable performance in complex backgrounds and fine-grained recognition tasks. These findings indicate that YOLOv8-ODConv provides an effective technical approach for the digital documentation, automatic recognition, and intelligent analysis of rock art cultural heritage.