Abstract: A CME-based few-shot detection model with enhanced multiscale deep feature is proposed to address the problems that existing few-shot detection models have insufficient consideration of global semantic information of images and degradation of detector performance due to varying input image sizes. Firstly, the model is trained with a large amount of labeled base class data and a multilayer convolutional neural network based on residual jumping and a multiscale feature enhanced module with good generalization, then the model is fine-tuned with a small amount of labeled new class data and base class data, and finally the fine-tuned model is used for target detection. To verify the effectiveness of the model, the VOC2007 and VOC2012 datasets are used to train and evaluate the model, and the relevant ablation experiments demonstrate that the introduction of a multilayer convolutional neural network with residual jump structure and the multi-scale feature enhanced module can further increase the accuracy of the model both alone and in combination. In comparison experiments with six representative small-sample target detection models, it is shown that the CME with multiscale deep feature deepening scores outperforms the state-of-the-art detector by an average of 4.75 percentage points.

Key words: few-shot learning, object detection, residual jumping, multiscale feature, deep feature

摘要： 针对现有的小样本目标检测模型存在对图像全局语义信息考虑不足、输入图像大小不一而导致检测器性能下降的问题，提出了多尺度深层特征加强的CME小样本目标检测模型。利用大量有标签的基类数据和基于残差跳跃的多层卷积神经网络及多尺度特征增强模块训练一个泛化性良好的模型，经过少量有标签的新类数据和基类数据对模型微调，利用微调后的模型进行目标检测。为验证模型的有效性，使用VOC2007和VOC2012数据集对模型进行训练和评估，相关消融实验证明了引入残差跳跃结构的多层卷积神经网络和多尺度特征增强模块的单独使用和组合使用均可进一步增加模型的准确率。在与6个具有代表性的小样本目标检测模型的对比实验中表明，多尺度深层特征加深的CME比最先进的检测器得分平均提高4.75个百分点。

关键词: 小样本学习, 目标检测, 残差跳跃, 多尺度特征, 深层特征