整体技术路线是：Single Feature Representation 单一特征表示，yolo Backbone 上增加一个重复帧检测分支一、无训练，直接使用yolo的特征优点：简单确定：性能准确率之类的，需要测试，因为yolo的特征是用来做目标检测的，不是用来做相似性对比的，因此不学习直接拿来用，效果需要实验后才知道。在ultralytics -yolo中如何提取特征层，ultralytics 已经有相关接口def_predict_once(self,x,profile=False,visualize=False,embed=None):"""Perform a forward pass through the network. Args: x (torch.Tensor): The input tensor to the model. profile (bool): Print the computation time of each layer if True. visualize (bool): Save the feature maps of the model if True. embed (list, optional): A list of layer indices to return embeddings from. Returns: (torch.Tensor): The last output of the model. """y,dt,embeddings=[],[],[]# outputsembed=frozenset(embed)ifembedisnotNoneelse{-1}max_idx=max(embed)forminself.model:ifm.f!=-1:# if not from previous layerx=