紧凑型单级目标检测网络

2020 IEEE 8th International Conference on Computer Science and Network Technology (ICCSNT) Pub Date : 2020-11-20 DOI:10.1109/ICCSNT50940.2020.9304979

Chen Xing, Xi Liang, Rongjie Yang

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引用次数: 4

摘要

无人机捕获的航拍图像中的目标由于体积小而难以检测，那些检测精度较高的神经网络过于复杂，无法在无人机搭载的计算机上实时运行。本文提出了一种残差网络与YOLOv3-Tiny相结合的网络，利用残差网络对不同层次的特征进行合并，以提高YOLOv3-Tiny的小目标检测性能。在实验中，该网络的mAP值比YOLOv3-Tiny高2.9。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Compact One-Stage Object Detection Network

The targets in aerial images captured by drones are difficult to detect due to their small size, those neural networks with better detecting accuracy are too complicated to run real-time job on drone-mounted computer. This paper proposes a network combined residual network and YOLOv3-Tiny, residual network is used to merge different level features for improving YOLOv3-Tiny's small object detecting performance. During the experiment, the proposed network gets 2.9 higher mAP than YOLOv3-Tiny.

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2020 IEEE 8th International Conference on Computer Science and Network Technology (ICCSNT)

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