摘要
针对因夜晚无可见光的条件下导致目标难以检测的问题,基于嵌入式平台应用设备,提出了无人目标检测的优化改进模型,提出了更优的检测速度与检测精度的模型。该优化改进后的模型以YOLOv3-tiny为基础框架,使用Small Batch-K-Means聚类算法计算anchor box数量;在网络关键位置中使用注意力机制,通过强化检测目标关注以提高抗干扰能力。在KAIST Multispectral Pedestrian Detection Benchmark数据集中的实验表明,该算法与传统YOLOv3-tiny算法相比在检测精度和速度上都有明显的提高。
Aiming at the problem that it is difficult to detect the target under the condition of no visible light at night,based on the application equipment of embedded platform,this paper puts forward the optimization and improvement model of UAV target detection,and puts forward a model with better detection speed and detection accuracy.The optimized and improved model takes YOLOv3-tiny as the basic framework and uses Small Batch-K-Means clustering algorithm to calculate the number of anchor box;the attention mechanism is used in the key position of the network to improve the anti-interference ability by strengthening the attention of the detection target.Experiments in KAIST Multispectral Pedestrian Detection Benchmark data set show that the algorithm has significantly improved the detection accuracy and speed compared with the traditional YOLOv3-tiny algorithm.
作者
刘信潮
房桐
杜保帅
赵景波
LIU Xinchao;FANG Tong;DU Baoshuai;ZHAO Jingbo(Qingdao University of Technology,Qingdao 266520,China)
出处
《现代信息科技》
2022年第10期73-77,共5页
Modern Information Technology
基金
国家自然科学基金:项目(51475251)
山东省自然科学基金:(ZR2013FM014)
山东省高等学校科技计划项目(J12LN37)
青岛市科技计划项目(15-9-2-109-nsh)。
