Target Detection of UAV Aerial Image Based on Rotational Invariant Depth Denoising Automatic Encoder

• Published in: Xibei Gongye Daxue Xuebao Vol. 38; no. 6; pp. 1345 - 1351
• Main Authors: Yang, Fengping, Ma, Bodi, Wang, Jinrong, Gao, Honggang, Liu, Zhenbao
• Format: Journal Article
• Language: English
• Published: EDP Sciences 01.12.2020
• Subjects: deep denoising automatic encoder; rotation-invariant; simulation experiment; target detection; uav aerial image
• ISSN: 1000-2758; 2609-7125
• DOI: 10.1051/jnwpu/20203861345

The method of using unmanned aerial vehicle (UAV) to obtain aerial image information of target scene has the characteristics of wide coverage, strong mobility and high efficiency, which is widely used in urban traffic monitoring, vehicle detection, oil pipeline inspection, regional survey and other aspects. Aiming at the difficulties of the object to be detected in the process of aerial image object detection, such as multiple orientations, small image pixel size and UAV body vibration interference, a novel aerial image object detection model based on the rotation-invariant deep denoising auto encoder is proposed in this paper. Firstly, the interest region of the aerial image is extracted by the selective search method, and the radial gradient of interest region is calculated. Then, the rotation invariant feature descriptor is obtained from the radial gradient feature, and the noise in the original data is filtered out by the deep denoising automatic encoder and the deep feature of the feature descriptors is extracted. Finally, the experimental results show that this method can achieve high accuracy for aerial image target detection and has good rotation invariance. 利用无人机航拍获取目标场景影像信息的方式，具有可低空作业、覆盖面积广、机动性强、效率高、不受地势环境阻碍等优点，广泛应用于军民用领域，军事领域包括威胁目标空中监视、目标搜索、目标打击，民用领域包括交通监测、灾难营救、管线巡检、区域勘测、边境巡逻等方面。无人机航空影像目标检测过程中，针对待识别目标具有多个角度、成像像素尺寸小、机体震动干扰强等困难，提出一种基于深度去噪自动编码器的目标检测模型。该模型通过进行选择性搜索，提取航空影像感兴趣区域，计算感兴趣区域的径向梯度特征，得到旋转不变特征向量，利用深度去噪自动编码器滤掉原始数据中的噪声，并提取特征向量的深层特征。在国际无人机低空航空影像标准数据集UAV123以及德国宇航院的慕尼黑无人机航空影像集DLR 3K上开展了识别实验，结果表明，针对航空影像目标包括地面车辆、行人、海面船只等，所提方法能够达到90%以上的识别精度，在精准率、召回率、F1调和值等指标上领先于现有方法。