基于最优密集YOLO方法的无人机下车辆检测

Zhi Xu, Haochen Shi, Ning Li, Chao Xiang, Huiyu Zhou
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引用次数: 41

摘要

本文设计了一种基于无人机平台下小目标检测的深度神经网络模型。鉴于YOLO等单阶段检测模型结构新颖,具有较大的工业应用潜力,本文提出了一种基于YOLOv2结构的新型检测模型。针对小目标漏检问题,提出了一系列适用于鸟瞰角度下小型车辆检测的改进方案,包括密集拓扑和最优池化策略,能够实现实时检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vehicle Detection Under UAV Based on Optimal Dense YOLO Method
In this paper, a deep neural network model based on small target detection under UAV platform is designed. Due to the One-stage detection model like YOLO having novel structure and great industrial application potential, this paper proposes a new model of detection based on YOLOv2 structure. Faced with missed detection problem of small target, a series of improved schemes are proposed, which are suitable for small vehicles’ detection under aerial view angle, and can achieve real-time detection, including dense topology and optimal pooling strategy.
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