This study proposes a remote sensing image detection method based on deep learning to solve the issues of human intervention, slow speed, and low accuracy associated with the traditional remote sensing image detection algorithm. A dense connected network is considered to completely use the features extracted from each layer and reduce the network inference time. Further, an expanding block structure with a large perceptive field is adopted, and the low- and high-level feature informations of the network are combined based on the expanding block structure and deconvolution network. Thus, the performance of multiscale object detection for remote sensing images is improved. The experimental results denote that the proposed method exhibits high accuracy and short detection time, especially during the detection of small objects.