Object detection and analysis in remote sensing images is a critical research subject for many businesses and agencies. At present, object detection based on convolutional neural network (CNN) in natural scenes has good performance. Due to the large number of small objects and similar characteristics between the objects in the VisDrone dataset, the current model cannot extract more small-scale features. Therefore, this paper proposes a stronger feature extraction FasterRCNN (SFE-FasterRCNN) that advances a feature extraction strengthening network to enhance the feature learning ability for different objects. Specifically, the pixel proposal network (PPN) is proposed by combining the low-resolution and strong semantic features with high-resolution and weak semantic features through a top-down approach and reusing these fusion blocks vertically to construct a comprehensive semantic feature map. Then hyperbolic pooling is proposed to minimize the loss of feature information during the activation mapping process. Finally, data clustering is used to adaptively generate better object proposals according to the characteristics of the dataset. Experimental results on the VisDrone dataset show that our method has excellent detection results.