In order to achieve unmanned-mode intelligent collection of tank target information on the frontline battlefield in information-based warfare,an improved algorithm based on YOLOv4 is proposed for ground tank target detection.Based on the original YOLOv4 object recognition algorithm,the multi-layer feature stitching module is employed to enhance the transmission and flow of feature information.The global information acquisition module is used to better capture global feature information.The multi-scale information fusion module is utilized to expand the scale of feature fusion.The decoupling detection head module is added to decouple the tasks of target classification and position regression,enabling more thorough network learning.The experimental results show that:1) Compared with the YOLOv4 algorithm,the improved YOLOv4_Modify algorithm achieves higher recognition accuracy,with a 10.2 percentage points increase in Recall and a 4.3 percentage points increase in mAP;and 2) The improved YOLOv4_Modify algorithm can accurately identify tank targets of different scales in complex environments,addressing the original algorithms drawback of missed detection for small tank targets,and providing visual technological support for information-based warfare.