To address the problems of low detection accuracy, high missed detection rate in small target detection, and low detection efficiency in practical application scenarios in satellite remote sensing image target detection, a multi-scale target detection method based on improved YOLOv7 for satellite remote sensing images is proposed. In the detection network, the focus is on improving the detection capability of small targets by adding a ConvNeXt Block (CNeB) with class attention, which enhances the ability of extraction and utilization of fine-grained features of small targets. At the same time, a post-processing mechanism is proposed to establish the mutual relationship between small and large targets, enabling the detection of multiple-scale targets using a single model. Experimental results show that on four small targets in the TGRS-HRRSD dataset, the improved detection model achieved an average improvement of 16.6% in mean average precision compared to the original YOLOv7. In specific large target detection tasks, the post-processing mechanism reduced the time by 70% compared to YOLT while maintaining accuracy. Compared to mainstream remote sensing image detection methods, this method is more accurate and faster in detecting multi-scale targets.