融合多尺度多头自注意力和在线难例挖掘的小样本硅藻检测

硅藻训练样本量较少时，检测精度偏低，为此在小样本目标检测模型TFA（Two-stage Fine-tuning Approach）的基础上提出一种融合多尺度多头自注意力（MMS）和在线难例挖掘（OHEM）的小样本硅藻检测模型（MMSOFDD）。首先，结合ResNet-101与多头自注意力机制构造一个基于Transformer的特征提取网络BoTNet-101，以充分利用硅藻图像的局部和全局信息；然后，改进多头自注意力为MMS，消除了原始多头自注意力的处理目标尺度单一的局限性；最后，引入OHEM到模型预测器中，并对硅藻进行识别与定位。把所提模型与其他小样本目标检测模型在自建硅藻数据集上进行消融及对比实验。实验结果表明：与TFA相比，MMSOFDD的平均精度均值（mAP）为69.60%，TFA为63.71%，MMSOFDD提高了5.89个百分点；与小样本目标检测模型Meta R-CNN和FSIW相比，Meta R-CNN和FSIW的mAP分别为61.60%和60.90%，所提模型的mAP分别提高了8.00个百分点和8.70个百分点。而且，MMSOFDD在硅藻训练样本量少的条件下能够有效地提高检测模型对硅藻的检测精度。

Few-shot diatom detection combining multi-scale multi-head self-attention and online hard example mining

The detection precision is low when the diatom training sample size is small， so a Multi-scale Multi-head Self-attention （MMS） and Online Hard Example Mining （OHEM） based few-shot diatom detection model， namely MMSOFDD was proposed based on the few-shot object detection model Two-stage Fine-tuning Approach （TFA）. Firstly， a Transformer-based feature extraction network Bottleneck Transformer Network-101 （BoTNet-101） was constructed by combining ResNet-101 with a multi-head self-attention mechanism to make full use of the local and global information of diatom images. Then， multi-head self-attention was improved to MMS， which eliminated the limitation of processing single object scale of the original multi-head self-attention. Finally， OHEM was introduced to the model predictor， and the diatoms were identified and localized. Ablation and comparison experiments between the proposed model and other few-shot object detection models were conducted on a self-constructed diatom dataset. Experiment results show that the mean Average Precision （mAP） of MMSOFDD is 69.60%， which is improved by 5.89 percentage points compared with 63.71% of TFA； and compared with 61.60% and 60.90% the few-shot object detection models Meta R-CNN and Few-Shot In Wild （FSIW）， the proposed model has the mAP improved by 8.00 percentage points and 8.70 percentage points respectively. Moreover， MMSOFDD can effectively improve the detection precision of the detection model for diatoms with small size of diatom training samples.