一种基于注意力机制的小目标检测深度学习模型

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...

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

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.