A small object detection algorithm of remote sensing image based on improved Faster R-CNN
Object detection in remote sensing images is a critical issue in the field of object detection.Currently,most object detection models that using deep learning add attention mechanism during the u-nidirectional feature fusion process,enhancing various types of objects indiscriminately and failing to highlight small objects.In order to achieve better detection results,an asymmetric high and low-level modulation mechanism is introduced,constructing feature maps that consider shallow detail information and advanced semantic information with the aim of enhancing the characteristics of small objects.Addi-tionally,the DIoU loss function is used instead of the original SmoothL1 loss function to improve model detection accuracy and convergence speed.Furthermore,flexible context information is introduced into in the region of interest classification task to improve the accuracy of small objects classification.Experi-ments demonstrate that the proposed method achieves good performance on DIOR and NWPU VHR-10 datasets.
deep learningsmall object detectionremote sensing imageasymmetric high-low layer modulationcontext information
国家科技期刊平台
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