Remote sensing images have the characteristics of a multi-scale, high resolution, and complex background, making the accuracy of small target detection too low. We propose an improved method based on the “you-only-look-once” v5 network model to make it more suitable for small target detection. First, we design a four-scale detection layer that improves the depth of the network and the accuracy of small target detection. Then, we introduce complete intersection over union-non-maximum suppression (NMS) to replace the commonly used NMS as a suppression of the prediction bounding box, and it also considers the overlap area, center distance, and aspect ratio to improve the accuracy of predicting bounding boxes. Furthermore, the remote sensing image is segmented into several fixed-size smaller images, making its size suitable for the input of the model to avoid target loss caused by directly scaling the size. The experimental results show that, compared to the four available detection methods, the improved algorithm significantly improves the detection accuracy of targets from small aircraft and ships in complex environments. The miss detection rate is also greatly reduced, especially for small targets, which verifies the effectiveness and robustness of the algorithm. Moreover, the detection rate has been greatly improved, which can meet the demand for real-time detection.

中文翻译：

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改进YOLOv5网络在遥感目标检测中的应用

遥感图像具有多尺度、高分辨率、背景复杂的特点，使得小目标检测精度过低。我们提出了一种基于“you-only-look-once”v5网络模型的改进方法，使其更适合小目标检测。首先，我们设计了一个四尺度的检测层，提高了网络的深度和小目标检测的准确性。然后，我们引入了union-non-maximum抑制（NMS）上的完全交集来代替常用的NMS作为预测边界框的抑制，并且还考虑了重叠面积、中心距和纵横比来提高预测精度预测边界框。此外，遥感图像被分割成几个固定大小的较小图像，使其尺寸适合模型的输入，避免直接缩放尺寸造成的目标损失。实验结果表明，与现有的四种检测方法相比，改进算法显着提高了复杂环境下小型飞机和舰船目标的检测精度。漏检率也大大降低，特别是对于小目标，验证了算法的有效性和鲁棒性。而且检测率也有了很大的提高，可以满足实时检测的需求。漏检率也大大降低，特别是对于小目标，验证了算法的有效性和鲁棒性。而且检测率也有了很大的提高，可以满足实时检测的需求。漏检率也大大降低，特别是对于小目标，验证了算法的有效性和鲁棒性。而且检测率也有了很大的提高，可以满足实时检测的需求。