To improve the detection speed of the YOLOv3-CS algorithm for remote sensing image target detection, an adaptive sparse factor adjustment algorithm based on the γ parameter of the Batch Normalization (BN) layer is proposed. YOLOv3-CS was pruned to obtain YOLOv3-CSP using γ as the basis for determining the channel importance. The experimental results show that the proposed pruning method reduces the model size by 95.92%, while increasing the detection speed by 173%, when the mean Average Precision (mAP) loss of YOLOv3-CS is 0.22%. The YOLOv3-CSP can be applied to certain occasions requiring high detection accuracy and real-time performance.