Multi-scale object detection (MOD) is one of the remaining challenges for satellite imagery. To improve the performance of MOD task, YOLT (You Only Look Twice) has achieved a good accuracy in high resolution remote sensing images. Motivated by the state-of-art object detection method for satellite imagery, we explored and achieved the state-of-the-art accuracy based on the standard YOLT for MOD task by providing a novel method with enough experimental results and model comparison on the typical multi-scale satellite imagery dataset. First, we divide objects into three categories according to the scale of objects. Then, different training strategies are used to train the classifier and detector for different scale objects. Finally, multi-scale detection chips are stitched and fused to get more accurate localization and classification as the final predicted results for MOD in satellite imagery. Experiments have been conducted over dataset from the second stage of AIIA ¹ Cup Competition of Typical Object Recognition for Satellite Imagery in Small Samples compared with the standard YOLT and Faster R-CNN, which demonstrates the effectiveness and the comparable detection performance of our proposed pipeline.