Target Detection of SSD Aircraft Remote Sensing Images Based on Anchor Frame Strategy Matching

Abstract

Aiming at the problem that the accuracy and real-time performance of current aircraft remote sensing image target detection algorithms cannot be balanced, a target detection algorithm based on single shot MultiBox detector (SSD) is proposed for anchor frame scale densification and anchor frame strategy matching. The algorithm uses an improved deep residual network to replace the original feature extraction network of the SSD algorithm. Combined with the small-scale and dense features of aircraft remote sensing images, this paper redesigns the size and proportion of anchor frame and adds a feature layer containing two scales. Then, the anchor frame densification operation is performed on each feature layer to make the anchor frame laying density of the feature layer basically equal, and to improve the probability of matching the anchor frames of different scales to the real target. On the issue of the large gap in the number of positive sample anchor frames of different scales, an anchor frame strategy matching method that makes the number of positive sample anchor frames of different scales tend to the overall positive sample average is proposed, which improves the effectiveness of training and robustness of target detection to a certain extent. Related experiments are conducted on the aircraft remote sensing dataset, the average precision reaches 91.15%, and the frame per second is 33.4. The results show that the improved algorithm can not only increase the detection accuracy on the basis of adding fewer training parameters, but also retain the real-time detec-tability of the SSD algorithm.

Keywords

• |target detection|remote sensing image|real-time detection|anchor box matching