Improved SSD based aircraft remote sensing image target detection

Aiming at the problem that the accuracy and real-time performance of current aircraft remote sensing image target detection algorithms can not be balanced, an aircraft remote sensing image target detection algorithm based on single shot multibox detector(SSD) is proposed. Firstly, the improved deep residual network is used to replace the skeleton network of SSD. For the problems of lack of feature information association between feature maps, and different channels from feature maps have no weight values, this paper designs a new feature pyramid network with feature receptive field enhancement module and attention mechanism module. The network is used to fuse the feature information of different levels and the weight coefficients between the training feature channels, so that both the deep network and the shallow network can obtain fusion features with rich structural levels, which provides a good prerequisite for the classification and positioning of the subsequent network. In addition, the focus classification loss function is also used in the improved SSD algorithm to solve the problem of imbalance between positive and negative samples. Related experiments are carried out on the aircraft remote sensing data set, and the average accuracy reaches 92.45%, and the frame per second is 35.6. The results show that the improved SSD algorithm can balance high detection accuracy and real-time performance at the same time.