To enhance detection performance for various targets in remote sensing images, an improved algorithm based on LSKNet-S is proposed. This algorithm notably advances the network's ability to detect remote sensing targets by integrating features across diverse receptive field levels. First, a multiscale feature fusion module is designed to strengthen the model's ability to extract global contextual information, with improvements to the multilayer perceptron. Concurrently, a lightweight local visual center module is introduced, enhancing the model's sensitivity to local features. The integration of these modules facilitates effective multiscale feature extraction and fusion within the model. Additionally, a scale-enhancing upsampling operation is incorporated within the detection head, which elevates the feature map resolution, allowing the model to more effectively capture detailed information on various targets within remote sensing images. Experimental results indicate that the proposed algorithm improves the mean average precision (mAP) by 3.43 percentage points on the HRSC2016 dataset and by 1.12 percentage points on the DIOR-R dataset, outperforming current mainstream algorithms. These results confirm the effectiveness of the proposed algorithm in remote sensing object detection contexts.