Aircraft Target Detection in Remote Sensing Images Based on Improved YOLOv7-Tiny Network

To address the challenges posed by complex backgrounds and target scale variations in remote sensing aircraft detection, we propose an improved YOLOv7-tiny network designed to boost both detection accuracy and the model’s ability to generalize across varying conditions. Complex backgrounds hinder effective target feature extraction, while variations in target scale complicate the real-time detection of objects at different sizes. To address these issues, we introduce a multi-head dynamic joint self-attention mechanism that combines self-attention, channel attention, and spatial attention, thereby enhancing the model’s ability to capture crucial target features. Furthermore, we design an enhanced feature extraction module that expands the receptive field and improves the model’s ability to extract features from multi-scale targets. In addition, we constructed diverse remote sensing aircraft detection datasets and applied a range of data augmentation techniques to improve the robustness and adaptability of the model in real-world scenarios. The experimental results demonstrate that the enhanced YOLOv7-tiny model achieves a mAP of 93.41%, which is a 2.98% improvement over the original model. The model also achieves precision and recall rates of 90.44% and 87.81%, respectively, with a FPS rate of 23.31. These results highlight the model’s excellence in both precision and real-time performance, making it highly effective for remote sensing aircraft detection and fine-grained feature recognition, particularly in complex environments where real-time processing is crucial.