This paper proposes a fractional-order Gabor transform convolution-based algorithm for aircraft target detection in remote sensing images, addressing challenges such as background noise, target size, and rotation angle which interfere with feature extraction. Firstly, in the feature extraction network, a novel and efficient attention feature extraction module is introduced by combining an efficient layer aggregation network with a convolutional block attention module, enhancing both the quality and efficiency of feature extraction. Secondly, a fractional-order Gabor transform convolution module is constructed in the feature fusion network to emphasize fine-grained details such as the edges, textures, and orientations of aircraft targets, thereby improving feature fusion. Finally, a learnable dynamic detection head is applied in the detection layer, where a scale-aware attention module strengthens attention to multi-scale targets, a spatial-aware attention module enhances spatial position discrimination, and a task-aware attention module facilitates more precise distinction of task-specific requirements. Experimental results on the DOTAv1 dataset demonstrate that the proposed method achieves a detection accuracy of 96.2%, which is 2.2% higher than the baseline YOLOv7 model. The method also has a smaller model weight, with a notable improvement in detection accuracy in complex scenarios. This approach provides a more efficient solution for aircraft target detection in remote sensing images.