关键词: 群养猪, 身份识别, ResNeXt50, 深度学习, 双向特征金字塔网络, 双重注意力机制

Abstract: Presently pig aggression recognition is still in the research stage of group level/pairwise level，and the identification of individual pig has become a necessary condition for further realizing individual level aggression recognition. In order to solve the problem of difficult identification caused by body deformation，occlusion，overlapping and other factors under pig aggression situation，an improved deep learning algorithm based on ResNeXt50（Residual networks with next‑50）was proposed to recognize pig identity in aggression situation. 18 000 frames were generated from the labelled 600 1‑second aggressive video episodes as the dataset.Firstly，the GECA（Ghost‑based efficient channel‑coordinate attention）module was embedded in the backbone network ResNeXt50 to enhance feature discriminative ability.Secondly，bidirectional feature pyramid network（BiFPN）was introduced to enhance the fusion capability of multi‑scale features.Then，position attention mechanism（PAM）was cascaded after BiFPN to improve the discrimination of global spatial features generated by pig body deformation，and channel attention mechanism（CAM） was used to optimize feature utilization through channel adaption.Finally，Fovea Head was used to recognize the identity of pigs under aggression situation.The identity of pigs could be recognized by using the proposed algorithm GBPC‑ResNeXt50（GECA‑BiFPN‑PAM‑CAM‑ResNeXt50）with a mean average precision（mAP）of 95.6%，which was 3.8 percentage points higher than that of the benchmark network ResNeXt50.The result indicates that this method can be used to recognize the identity of individual pig under aggression situation.This provides a foundation for promoting the conversion of pig aggression recognition from group/pairwise level to individual level，and also provides a reference for identification of other livestock under aggression situation.

Key words: Group?housed pigs, Identification, ResNeXt50, Deep learning, Bidirectional feature pyramid network, Double attention mechanisms