一种基于注意力机制的小目标检测深度学习模型

A deep learning model of small object detection based on attention mechanism

小目标检测用来识别图像中小像素尺寸目标。传统目标识别算法泛化性差,而通用的深度卷积神经网络算法容易丢失小目标的特征,对小目标识别的效果不甚理想。针对以上问题,提出了一种基于注意力机制的小目标检测深度学习模型AM-R-CNN,该模型在ResNet101主干网络和候选区域生成网络中使用了通道域注意力和空间域注意力,通道域注意力模块实现了通道维度上的特征加权标定,空间域注意力模块实现了空间维度上的特征聚焦,从而提升了小目标的捕获效果。此外,模型使用数据增强技术和多尺度特征融合技术,保证了小目标特征提取的有效性。在遥感影像数据集上的识别船只实验表明,注意力模块可带来小目标检测的性能提升。

Small target detection is used to identify small pixel size targets in image.Traditional target recognition algorithms has poor generalization ability,and general depth convolution neural network algorithms are easy to lose the characteristics of small target,so these algorithms are not ideal for small target recognition.To solve the above problems,a deep learning model of small target detection based on attention mechanism is proposed.The model uses channel attention and spatial attention in resnet101 backbone network and region proposal network.The channel attention module implements feature weighting calibration in channel dimension,and the spatial attention module realizes feature focusing in spatial dimension,thus improving the capture effect of small targets.In addition,the model uses data enhancement technology and multi-scale feature fusion technology to ensure the effectiveness of small target feature extraction.The experiment of ship recognition in remote sensing image data set shows that the attention module can improve the performance of small target detection.