Recently the analysis of remotely sensed images has played a vital role in various aspects of research. The current researches ignore the unique prior knowledge in remote sensing images and do not consider exploring the contextual information of the object, while the existence of multi-scale and high image resolution of objects in remote sensing images also affects the accuracy of the object detection task. Based on the above problems, this paper proposes a object detector DCI-Net (Dynamic Context-Aware IoU Network) based on remote sensing images, in which the proposed CASK (Context-Aware Selective Kernel) module can explicitly model the interdependence between the convolutional feature channels. A loss function Pi_IoU is proposed, which adaptively adjusts the penalty factor in combination with the size of the detected object. A DySample module is introduced, which is able to effectively extract and utilize the spatial structure features. The model in this paper improves the detection accuracy of complex objects in remote sensing images. On the DIOR dataset, compared with the baseline model YOLOV9, the accuracy is improved by 0.6%, the number of parameters is decreased by 4%, and the floating point operation speed is improved by 27.8%.