Object detection is one of the critical technologies in intelligent remote sensing, integrating sensor design, image acquisition, and analysis to locate targets in images, especially crucial for the perception and communication of unmanned aerial vehicle (UAV) systems. In recent years, significant progress has been made in object detection based on deep learning, such as convolutional neural networks (CNNs). However, existing methods, particularly in adapting to remote sensing sensor systems, still face two main challenges: deep learning framework overfitting and inefficient multiscale object detection. To bridge the gap between remote sensing sensor design and image analysis algorithms, we propose a lightweight detector based on context feature adaptive redetecting, ReAC-DETR, an enhanced version of the baseline detector Faster-RCNN. ReAC-DETR further improves with Fast-RCNN by optimizing the feature extraction and fusion branches, making it more suitable for small object detection. It also alleviates the problem of vague object features in remote sensing perception through an object saliency enhancement algorithm. Finally, we propose a spatial context adaptive analysis algorithm to enhance the detector’s capability to adjust to multiscale object detection and improve detection precision. ReAC-DETR can provide a more robust detection technology for remote sensing, enabling the system to adapt to various shooting devices, methods, targets, and environments. We tested ReAC-DETR on two challenging datasets and achieved excellent performance.