Research On Remote Sensing Image Target Detection Based On Single-stage Detection Network

Object detection is to identify and locate objects of interest in the image,which is a basic task for image understanding and application.Remote sensing image object detection is an important research direction of object detection application.It can be widely used in urban planning,disaster monitoring,military guidance,drone navigation and other fields,and has high military and civilian value.With the development of object detection theory based on deep learning,the performance of remote sensing image object detection has been greatly improved.However,the characteristics of remote sensing image,such as high resolution,large scale variation and uneven distribution,complex scene and difficult to distinguish from the object,increase the research difficulty of remote sensing image object detection,resulting in the development of remote sensing image object detection still lags behind the development of ordinary image object detection.Based on existing research and deep learning object detection theory,Improves the single-stage detection network to make it better used in the field of remote sensing image object detection,and forms a fast and effective remote sensing image object detector based on the single-stage detection network.There are three main innovations.(1)In the convolution neural network,the shallow feature map contains rich details but lacks semantic information,while the deep feature map contains rich semantic information but lacks detailed information.In order to make use of the advantages of different depth feature maps and detect multi-scale objects,a single-stage object detection model using convolution filter pyramid and atrous convolution is proposed.Firstly,multi-layer feature maps of different resolutions are fused by pixel-by-pixel addition,and then feature maps of different stages are concatenated to form the fusion feature layer with rich semantic information and detail information as the prediction feature layer of the model.Secondly,the convolution filter pyramid structure is introduced into the anchor mechanism to detect multi-scale objects.At the same time,atrous convolution is used to reduce the parameters added by large-scale convolution filter and reasonably reduce the number of anchors.The experimental results show that the proposed model not only has faster detection speed and higher accuracy,but also solves the problems of small objects and occluded objects that are difficult to detect.(2)A one-stage remote sensing object detection model:Feature Attention Pyramid Network(FAPNet).Firstly,the FAPNet model uses the feature attention pyramid module to fuse the feature information of different feature layers to form multiple prediction feature layers with different resolutions,which improves the detection accuracy of objects at different scales in remote sensing images.Secondly,the weak supervised attention module is used to adjust the feature distribution of the prediction feature layer,strengthen the object region features,and improve the detection accuracy of occluded objects in remote sensing images.In the feature attention pyramid module,the feature maps of different layers are fused by means of channel concatenation,and the SE(Squeeze-and-Excitation)attention module is added to refine the fusion feature map in the channel dimension to adaptively adjust the richness of different feature layers in the fusion feature map.In the weak supervised attention module,the prediction values of segmentation results are taken as the spatial attention weight,adjust the feature distribution of prediction feature layers and improve the performance of object detection network.The experimental results show that the proposed model surpasses the baseline model and other advanced models in two remote sensing image data sets and achieves better detection accuracy.(3)Scale variation across object instances and occlusion issue are still challenging research topics in the remote sensing object detection task.Feature pyramids are an effective method for detecting objects at different scales.The way of transferring feature information layer by layer will lose feature information in the feature pyramids.Therefore,a feature pyramid network with shortcut connections is proposed,which can enhance the semantic and detail information of each feature layer of feature pyramid network.Moreover,using spatial attention to strengthen the possible object area feature is an effective method to solve the occlusion problem.But the available spatial attention will strengthen feature regions that produce the imprecise prediction results simultaneously,thus will interfere with the final prediction results.For this purpose,an anchor-based spatial attention module is proposed.It mainly strengthens feature regions that are more likely to produce accurate prediction results.The feature pyramid network with shortcut connections and the anchor-based spatial attention module are embedded into the RetinaNet to form an end-to-end single-stage remote sensing object detector,namely AANet(Anchor-based Attention Network).The experimental results show that AANet is a fast and effective single-stage remote sensing object detector.