随着科学技术的进步,人工智能广泛应用于各个学科,并产生了惊人的成果。目标检测算法的研究显著提高了无人机的性能和作用,在预防森林火灾方面发挥着不可替代的作用,疏散拥挤的人,测量和救援探险家。在这个阶段,在无人机中部署的目标检测算法已经应用到生产和生活中,但使检测精度更高、适应性更好仍是研究者继续研究的动力。在航拍图像中,由于拍摄高度高,小尺寸,分辨率低,功能少,传统的目标检测算法难以检测到。在本文中,UN-YOLOv5s算法能较好地解决小目标检测的难题。更精确的小目标检测(MASD)机制,大大提高了中小目标的检测精度,将多尺度特征融合(MCF)路径相结合,融合图像的语义信息和位置信息,以提高新颖模型的表达能力。引入新的卷积SimAM残差(CSR)模块,使网络更加稳定和集中。在VisDrone数据集上,你只看一次v5s(UN-YOLOv5s)的平均平均精度(MAP)比原始算法高8.4%。与同一版本相比,YOLOv5l,mAP增加了2.2%,每秒千兆浮点运算(GFLOP)减少了65.3%。与同系列的YOLOv3相比,mAP增加了1.8%,GFLOP减少了75.8%。与同系列的YOLOv8s相比,mAP的检测精度提高了1.1%。
With the progress of science and technology, artificial intelligence is widely used in various disciplines and has produced amazing results. The research of the target detection algorithm has significantly improved the performance and role of unmanned aerial vehicles (UAVs), and plays an irreplaceable role in preventing forest fires, evacuating crowded people, surveying and rescuing explorers. At this stage, the target detection algorithm deployed in UAVs has been applied to production and life, but making the detection accuracy higher and better adaptability is still the motivation for researchers to continue to study. In aerial images, due to the high shooting height, small size, low resolution and few features, it is difficult to be detected by conventional target detection algorithms. In this paper, the UN-YOLOv5s algorithm can solve the difficult problem of small target detection excellently. The more accurate small target detection (MASD) mechanism is used to greatly improve the detection accuracy of small and medium targets, The multi-scale feature fusion (MCF) path is combined to fuse the semantic information and location information of the image to improve the expression ability of the novel model. The new convolution SimAM residual (CSR) module is introduced to make the network more stable and focused. On the VisDrone dataset, the mean average precision (mAP) of UAV necessity you only look once v5s(UN-YOLOv5s) is 8.4% higher than that of the original algorithm. Compared with the same version, YOLOv5l, the mAP is increased by 2.2%, and the Giga Floating-point Operations Per Second (GFLOPs) is reduced by 65.3%. Compared with the same series of YOLOv3, the mAP is increased by 1.8%, and GFLOPs is reduced by 75.8%. Compared with the same series of YOLOv8s, the detection accuracy of the mAP is improved by 1.1%.