Window Anchored Offset Constrained Dynamic Snake Convolutional Network for Aerial Small Target Detection
To obtain the key and effective information from limited features of small targets and improve the localization ability and detection accuracy of small targets,a window anchored offset constrained dynamic snake convolutional network for aerial small target detection is proposed.Firstly,the offset constrained dynamic snake convolution is constructed.By dynamical offsetting in different directions,the constrained snake convolution kernel adaptively focuses on feature regions of different sizes and shapes,making feature extraction concentrate on tiny local structures and thereby facilitating the capture of small target features.Secondly,by employing two-stage multi-scale feature fusion method,feature alignment fusion and injection are performed on different layer-order feature maps to enhance the fusion of the underlying detail information and the high-level semantic information,and strengthen the transmission of target information of different sizes.Thus,the detection capability of the method for small targets is improved.Meanwhile,the window anchored bounding box regression loss function is designed.The function performs the bounding regression based on the auxiliary bounding box and the minimum point distance to achieve more accurate regression results and enhance the small target localization capability of the model.Finally,comparative experiments on three aerial photography datasets show that the proposed method makes the improvements with different degrees in small target detection performance.
Small Object DetectionFeature ExtractionFeature FusionMulti-scale FeaturesBoun-ding Box Regression Loss Function
万方数据
维普
