关键词: 小目标检测, 深度学习, 遥感图像, 注意力机制

Abstract: 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.

Key words: small object detection, deep learning, remote sensing image, attention mechanism