CMM-DETR: an efficient transformer-based detector for remote sensing measurement applications

Abstract Object detection in remote sensing plays an important role in measurement-oriented applications such as emergency monitoring, precision agriculture, and traffic observation. However, complex backgrounds, dense target distributions, and small object sizes remain challenging for reliable perception. In this work, we propose CMM-DETR, an efficient Transformer-based detector that improves the RT-DETR baseline through three components: CSGVMNet, modulation re-parameterization fusion module (MRFusion), and multi-scale feature pyramid network (MSFPN). Specifically, we structurally improve the EfficientVIM block by leveraging a gated channel selection mechanism and cross-stage partial-style connections, and further propose the CSGVM block to construct a new backbone network, CSGVMNet. To enhance feature aggregation, we develop MRFusion, which leverages the cross-scale fusion property of modulation fusion module and adaptively reweights multi-level features to enable dynamic cross-scale interactions. We further introduce MSFPN, an improved multi-scale feature pyramid that promotes effective exchange between high-level context and low-level details. In addition, a high-resolution detection head is employed to better preserve spatial resolution while maintaining an adequate receptive field, improving localization and recognition for small objects in crowded scenes. On VisDrone2019, CMM-DETR improves mAP50 and mAP50:95 by 6.4% and 5.4%, respectively, while reducing parameters by 18.6%. Experiments on UAVDT and RSOD further demonstrate the robustness and generalization of the proposed method. Overall, CMM-DETR provides an accuracy efficiency balanced solution for practical remote sensing measurement scenarios.