SPT:Swin Pyramid Transformer for Object Detection of Remote Sensing

Abstract

The task of object detection is a basic and highly concerned work in the field of computer vision.Because object detection in remote sensing has important application value in transportation,military,agriculture,etc.,it has also become a major research hotspot.Compared with natural images,remote sensing images are affected by many factors such as complex background interference,weather,irregularities,and small objects.It is extremely challenging to achieve higher accuracy in remote sensing image object detection tasks.This paper proposes a novel object detection network based on Transformer,swin pyramid Transformer(SPT).SPT uses a sliding window Transformer module as the backbone of feature extraction.Among it,the self-attention mechanism of Transformer is very effective for detecting objects in a chaotic background,and the sliding window mode efficiently avoids a large number of square-level complexity calculations.After obtaining the feature map extracted by the backbone network,SPT uses a pyramid architecture to fuse different scale and semantic features,pithily reducing the loss of information between feature layers,and capturing the inherent multi-scale hierarchical relationship.In addition,this paper proposes self-mixed Transformer(SMT) module and cross-layer Transformer(CLT) module.SMT re-renders the highest-level feature map to enhance object feature recognition and expression.According to the feature context interaction,the feature expressions of the pixels of each feature layer are rearranged by CLT,and the CLT module is integrated into the bottom-up and top-down dual paths of the pyramid to make full use of global and local information containing different semantics.Our SPT network model is trained and tested on the UCAS-AOD and RSOD datasets.Experimental results show that SPT is high-performing in remote sensing image object detection tasks,especially suitable for irregular and small target categories,such as overpass and car.

Keywords

• deep learning|object detection|remote sensing|attention mechanism|transformer