Ground object detection, based on remote sensing satellite imagery, provides the groundwork of numerous applications, so the detection accuracy is of vital importance. The background of remote sensing images is complex, the object size is various, and there are many small objects. In view of the above problems, a multi-attention object detection method (MA-FPN) based on multi-scale is proposed in this paper, which can effectively make the network pay attention to the location of the object and reduce the loss of small object information. According to feature pyramid network (FPN), we firstly put forward a global spatial attention module, which extracts spatial location-related information from shallow features and fuses it with deep features to enhance the position expression ability of deep features. Besides, the paper provides a pixel feature attention module: the multi-scale convolution kernel is employed to generate the feature map of the same size as the input, as well as channel attention is used to assign weights to each layer of feature maps to obtain pixel-level attention maps with good details. Experiments on NWPU, RSOD, and DOTA datasets show that the proposed algorithm outperforms state of the art methods.