As one of key tasks in the field of remote sensing image processing，object detection has always been a research hotspot.Although significant progresses have been made in this field，the deep learning methods still face significant challenges in dealing with scale changes and complex backgrounds in remote sensing images，which limits the further improvement of detection accuracy to some extent.To address this issue，an innovative object detection method for remote sensing images was proposed，which integrated a saliency guided image adaptive fusion module and improved Faster RCNN to enhance the accuracy of object detection.Firstly，in the image preprocessing stage，a saliency guided image adaptive fusion module was proposed，which effectively integrated the semantic information of the image and shallow fine-grained details，allowing the model to prioritize the object region while minimizing background interference.Secondly，after introducing MobileNetV3 as the feature extractor of Faster RCNN，an attention enhanced feature pyramid network was proposed，which combined attention with upsampling to further enhance target features and output high-quality feature maps，effectively improving the extraction effect of multi-dimensional features and providing more accurate and rich feature information for subsequent object detection tasks.Furthermore，a multi-scale region proposal network was designed，which can more accurately capture the features of objects of different sizes and shapes，thereby enhancing the expression ability of features and effectively improving the detection accuracy of targets.Finally，experiments on the DIOR and ROSD datasets demonstrated that the proposed network model exhibits higher detection accuracy compared to other advanced methods，fully demonstrating its superiority and effectiveness.