The application of deep learning techniques significantly enhances the accuracy and speed of tank target recognition in military operations , reducing misjudgments and omissions , thereby minimizing casualties and war losses. To address the limitations of large , complex , and computationally intensive models in terms of computing resources , storage , and energy consumption , a YOLOv5-based lightweight object detection system was proposed. This approach enriches gradient flow information and further accelerates computations through the C2f module based on attention mechanism. The combination of Lead Head and Aux Head balances positive and negative samples , improving the mod- el's ability to detect obscured small tank targets. Additionally , the utilization of FasterNet as the feature extraction net- work resolves issues related to high parameter quantity and computational demands. Experimental results demonstrate that compared to the original YOLOv5 , the improved model achieves a 1. 2% and 4. 2% increase in Map0. 5 and mAP0. 5 :0. 95 , respectively , while reducing parameters , GFLOPs , and Best. pt by 32. 3% , 27. 59% , and 26. 01% . The improved YOLOv5 model enables fast and accurate tank target recognition , making it more accessible for deploy- ment on mobile and embedded devices due to its lightweight nature.