Military conflicts have a significant impact on national security and the ecological environment. Effective detection methods for ground-moving objects in complex scenarios can be further utilized to assess damage and provide recommendations for security and environmental restoration. Current ground-moving object detection in optical remote sensing imagery struggles with balancing detection accuracy and real-time performance, hindering timely threat assessment. To address this, the study proposes ground-moving object detector (GMODet), a real-time detection method incorporating region awareness and semantic-spatial interaction to enhance the detection of partially occluded and fine-grained objects in complex environments. The framework includes three modules: the region awareness module (RAW), cross-scale context-aware feature aggregator (CCFA), and semantic-spatial progressive interaction module (SPIM), focusing on extracting discriminative features for contextual, multiscale, and semantic-spatial information. A new dataset, ground-based moving object dataset (GMOD), is constructed with four object types and high scene complexity, alongside experiments on the publicly available military vehicle remote sensing dataset (MVRSD). GMODet achieves the state-of-the-art performance, with mAP50, mAP75, and mAP scores of 65.5%, 48.5%, and 42.3% on the GMOD, outperforming the second-best results by 1.9%, 5.1%, and 1.5%, respectively. On the MVRSD, it achieves mAP50, mAP75, and mAP scores of 88.2%, 75.2%, and 61.7%, respectively. Notably, with an inference time of just 25 s on large-scale images ( $9152\times 9152$ pixels), GMODet showcases outstanding accuracy, speed, robustness, and generalization in ground-moving object detection.