This study proposes a remote-sensing image detection method based on context aware and sparse feature fusion to address the problems of missed detections and low detection accuracy. Such problems are caused by complex target background areas and insufficient small target feature information. First, a context aware unit was designed to mine spatial contextual feature information during feature extraction, enhancing the ability to capture small target features. Second, a sparse feature fusion strategy was developed to guide more effective feature fusion between shallow and deep features in the network by learning sparse representations. Finally, the Slim-Neck design paradigm was introduced at the network neck to reduce the complexity of the network model. The experimental results show that on the NWPU VHR-10 and DIOR remote sensing datasets, the proposed method reduces computational and parameter complexity by 3.1% and 6.3%, respectively, and improves detection accuracy by 1.6 percentage points and 2.6 percentage points, respectively, compared with YOLOv8s. And the proposed method performs better than the six mainstream detection methods.