基于Efficientnet的无人机车辆目标检测算法  

UAV Vehicle Object Detection Algorithm Based on Efficientnet

作　　者：江德港 江智 黄子杰 郭彩玲[2] 李柏林 JIANG Degang;JIANG Zhi;HUANG Zijie;GUO Cailing;LI Bailin(Graduate School of Tangshan,Southwest Jiaotong University,Tangshan,Hebei 063000,China;Key Laboratory of Intelligent Equipment Digital Design and Process Simulation of Hebei Province,Tangshan University,Tangshan 063000,China)

机构地区：[1]西南交通大学唐山研究院,河北唐山063000 [2]唐山学院河北省智能装备数字化设计及过程仿真重点实验室,河北唐山063000

出　　处：《计算机工程与应用》2023年第12期228-234,共7页Computer Engineering and Applications

摘　　要：针对无人机航拍图像中存在背景复杂,造成车辆漏检严重,检测精度低,以及现有深度网络存在参数量过多、检测速度慢的问题,提出一种基于Efficientnet的无人机车辆目标检测算法。引入轻量化网络Efficientnet作为YOLOv3模型特征提取网络,降低模型参数量,从而提高算法检测速度;采用K-means聚类算法对无人机车辆数据集真实框进行聚类,得到更为精确的边界框尺寸,提高检测的精度;使用CIoU边界框损失函数改进模型回归损失,提高模型收敛能力。实验结果表明,改进后的算法在自制无人机车辆数据集中mAP达到92.60%,FPS达到31.15,相对于原始算法分别提高了2.12个百分点和9.87,更加适用于无人机场景下的车辆检测任务。In response to the problems of complex background in the aerial images of the UAV which brings about seriously missed detection as well as poor detection precision,and excessive parameters as well as slow detection speed in the present deep network,an object detection algorithm of UAV vehicle based on Eficientnet is proposed.Firstly,Eficientnet,the lightweight network,is introduced as the feature extraction network of the YOLOv3 model,reducing the size of the model parameters and improving the detection speed of the algorithm.Secondly,the UAV vehicle dataset is clustered by the K-means algorithm to obtain the better bounding box size,thus improving the detection precision.Finally,with the CIoU bounding box loss function,the regression loss of the model is improved,thus enhancing the convergence ability of the model.Experimental results show that mAP of 92.60%and FPS of 31.15 can be achieved in the self-made UAV vehicle dataset based on the improved algorithm,which is 2.12 percentage points and 9.87 higher than the original algorithm,respectively.Therefore,it is more suitable for vehicle detection tasks in UAV scenarios.

关 键 词：无人机 车辆检测 YOLOv3 Effcientnet CIoU 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]