A multiscale object detection algorithm for satellite remote-sensing images is proposed to solve the problems of background confusion, low precision of small object detection, and high miss rate in multiscale object detection. The channel and spatial attention module is used in the backbone network, and the feature fusion network is redesigned to realize the multiple fusion of up-down-up sampling. The channel weight parameter is added to enable the network to pay more attention to critical channels, fully utilize different feature information levels, and enhance the detailed feature information. In a DIOR dataset, not only the detection effect of small objects but also the detection accuracy of objects in complex scenes is improved. Compared with that using YOLOv5m, the detection effect of some small or complex objects is improved significantly, the accuracy is improved by more than 4.5 percentage points, and the overall accuracy is improved by 3.1 percentage points.