College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Sensors (Basel). 2022 Aug 4;22(15):5825. doi: 10.3390/s22155825.
To solve the problem of low accuracy and slow speed of drone detection in high-resolution images with fixed cameras, we propose a detection method combining background difference and lightweight network SAG-YOLOv5s. First, background difference is used to extract potential drone targets in high-resolution images, eliminating most of the background to reduce computational overhead. Secondly, the Ghost module and SimAM attention mechanism are introduced on the basis of YOLOv5s to reduce the total number of model parameters and improve feature extraction, and -DIoU loss is used to replace the original DIoU loss to improve the accuracy of bounding box regression. Finally, to verify the effectiveness of our method, a high-resolution drone dataset is made based on the public data set. Experimental results show that the detection accuracy of the proposed method reaches 97.6%, 24.3 percentage points higher than that of YOLOv5s, and the detection speed in 4K video reaches 13.2 FPS, which meets the actual demand and is significantly better than similar algorithms. It achieves a good balance between detection accuracy and detection speed and provides a method benchmark for high-resolution drone detection under a fixed camera.
为了解决固定摄像机拍摄的高分辨率图像中无人机检测精度低、速度慢的问题，我们提出了一种结合背景差分和轻量级网络 SAG-YOLOv5s 的检测方法。首先，利用背景差分提取高分辨率图像中的潜在无人机目标，消除大部分背景以减少计算开销。其次，在 YOLOv5s 的基础上引入 Ghost 模块和 SimAM 注意力机制，减少模型参数总数，提高特征提取能力，并采用 -DIoU 损失取代原有的 DIoU 损失，提高边界框回归的准确性。最后，为了验证我们方法的有效性，基于公共数据集制作了一个高分辨率无人机数据集。实验结果表明，所提出的方法的检测精度达到 97.6%，比 YOLOv5s 高 24.3 个百分点，在 4K 视频中的检测速度达到 13.2 FPS，满足实际需求，明显优于类似算法。它在检测精度和检测速度之间实现了良好的平衡，为固定摄像机下的高分辨率无人机检测提供了一种方法基准。
