Optical remote sensing images are affected by the complexity of the background and the large amount of semantic information, and there are still certain deficiencies in detection accuracy and efficiency. This paper proposes an SSD300 network model based on Resnet50 for feature extraction, adding the attention mechanism CBAM module and feature fusion FPN module, and adopting the Soft-NMS strategy to select the final prediction frame to detect aircraft targets in remote sensing images more effectively. Finally, training on 2 150 aircraft remote sensing image data sets, when the IoU is 0.5 and 0.75, the average accuracy MAP reaches 92.54% and 63.44%, which are 5.04% and 11.38% higher than the previous algorithm model, and the detection speed reaches 13.4 FPS. Experimental results show that this method can effectively improve the detection ability of objects and quickly and accurately detect aircraft objects in the airport area, effectively reducing the missed detection rate of aircraft objects and improving detection accuracy and speed.