Presently, computer vision-based pig aggression recognition mainly adopts deep learning algorithms. However, these methods only recognize aggression from the pig herd/pairwise pigs and cannot determine which pigs are involved in aggression. Therefore, recognizing the identity of individual pigs helps to refine aggression recognition from the herd/pairwise level to the individual level. Regarding the influence of factors, e.g., body deformation, overlapping, etc., on the accuracy of pig identification in the aggression process of group-housed pigs, an improved YOLOv10s model IDBS-YOLOv10s for pig identification was proposed in this paper. Firstly, the InceptionNeXt-DCNv3 was used to replace the convolution in c2f in the backbone network to reduce the parameter and computational complexity of the model, thereby enhancing the ability of YOLOv10s network to extract features. Secondly, the weighted bidirectional feature pyramid network was used in the Neck layer to enhance the ability of the model to fuse different feature layers. Then, the SEAM attention mechanism was added before the detection head to enhance the ability of the model to extract key feature information of pig identities. Finally, the detection head v10detect was used to recognize the identity of individual pigs. The identification precision of this model was 94.3%, the recall was 93.7%, the mean average precision was 95.8%, and the model weight was only 15.2 MB. The results indicate that this method can be used to recognize the identity of pigs under aggression scenes.