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In recent years, general object detection on nature images leverages convolutional neural networks (CNNs) and vision transformers (ViT) to achieve great progress and high accuracy. However, unlike nature scenarios, remote sensing systems usually deploy a large number of edge devices. Therefore, this condition encourages detectors to have lower parameters than high-complexity neural networks. To this end, we propose an effective and lightweight detection framework of hybrid network, which enhances representation learning to balance efficiency and accuracy of model. Specifically, to compensate low precision caused by lightweight neural networks, we design a boundary-aware context (BAC) module and a frequency self-attention refinement (FSAR) module to improve detector performance in a hybrid structure. The BAC module enhances the local features of the object by fusing the spatial context information with the original image, which not only improves the model accuracy but also effectively solves the problem of multiscale objects. To alleviate the interference of complex background, the FSAR module adopts an adaptive technology to filter out redundant information at different frequencies to improve overall detection performance. The comprehensive experiments on remote sensing datasets, i.e., NWPU VHR-10, LEVIR, and RSOD, indicate that the proposed method achieves state-of-the-art performance and balances between model size and accuracy.