Ground objects in satellite images pose unique challenges due to their low resolution, small pixel size, lack of texture features, and dense distribution. Detecting small objects in satellite remote-sensing images is a difficult task. We propose a new detector focusing on contextual information and multi-scale feature fusion. Inspired by the notion that surrounding context information can aid in identifying small objects, we propose a lightweight context convolution block based on dilated convolutions and integrate it into the convolutional neural network (CNN). We integrate dynamic convolution blocks during the feature fusion step to enhance the high-level feature upsampling. An attention mechanism is employed to focus on the salient features of objects. We have conducted a series of experiments to validate the effectiveness of our proposed model. Notably, the proposed model achieved a 3.5% mean average precision (mAP) improvement on the satellite object detection dataset. Another feature of our approach is lightweight design. We employ group convolution to reduce the computational cost in the proposed contextual convolution module. Compared to the baseline model, our method reduces the number of parameters by 30%, computational cost by 34%, and an FPS rate close to the baseline model. We also validate the detection results through a series of visualizations. © 2024 by the authors.