Remote sensing target detection is an important aspect in the fields of environmental monitoring and circuit patrol. A remote sensing target detection algorithm based on YOLOX is proposed for the difficulties of remote sensing images with large target scale differences, blurred targets and high background complexity. First, a regional context aggregation module is proposed to expand the perceptual field using the dalited convolutions with different expansion rates to obtain multi-scale contextual information, which is beneficial to the detection of the small targets. Second, the feature fusion module is proposed, and two different scale transformation modules are used to achieve the fusion of features at different scales, fully fusing shallow location information with deep semantic information to improve the detection performance of the network for targets at different scales. Finally, a feature enhancement module is introduced to the multi-scale feature fusion network part and combined with the attention mechanism CAS [CA (coordinate attention) with SimAM (simple parameter-free attention module)] to make the network pay more attention to the target information and ignore the interference of complex background, while the shallow feature layer is fused with the deep detection layer for feature fusion to prevent the low detection performance affected by the loss of feature information at the prediction end. The experimental results show that the improved algorithm achieves 73.87% and 96.22% detection accuracy on DIOR and RSOD remote sensing datasets, which is 4.08 and 1.34 percentage points higher than the original YOLOX algorithm, and has superiority in both detection accuracy and detection speed compared with other advanced algorithms.