Abstract: Aiming at the problem of low accuracy of small target detection in remote sensing image under complex background, an new SSD detection algorithm based on dual attention mechanism model is proposed. The algorithm introduces the dual attention mechanism model in the front-end feature extraction network. It strengthens the effective feature information of small targets in the low-level feature map and suppresses the redundant semantic information to achieve adaptive feature learning. In addition, dilated convolution is introduced into the spatial attention model to ensure the sensitivity of convolution kernel and reduce the parameters of the network. The Focal loss function is introduced as the classification loss function of the improved algorithm to improve the imbalance of samples and increase the weight ratio of positive and difficult samples during training, it promotes the detection performance of the algorithms. The detection results of the remote sensing image data set NWPU VHR-10 show that the improved algorithm not only ensures the detection speed, but also improves the detection accuracy. Compared with the traditional SSD algorithm, the mAP of the improved SSD algorithm is increased by 2.25 percentage points to 79.65%.

Key words: deep learning, target detection, feature extraction, double attention mechanism model, dilated convolution, Focal loss function