使用改进的YOLOv4进行军事目标探测

IF 1.3 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS

IET Networks Pub Date : 2022-10-14 DOI:10.1109/IET-ICETA56553.2022.9971558

Jung-Hung Pan, Chiu-Chin Lin, Jen-Chun Lee, Chung-Hsien Chen

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引用次数: 0

摘要

提出了基于遥感图像的目标检测方法。该方法进一步提高了检测精度，降低了错误率。改进的YOLOv4是一种基于YOLO (YouOnly-Look-Once)目标检测方法的加速神经网络模型。它在执行时间和检测性能方面优于现有网络。实验结果表明，该方法提高了遥感图像中目标检测的平均精度(mAP)。因此，我们提出了一种新的高分辨率遥感图像自动目标检测系统。

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Using Modified YOLOv4 for Military Target Detection

We propose methods for object detection based on remote sensing images. This method further improves detection accuracy and decreases error rates. Modified YOLOv4 is an accelerated neural network model based on the YOLO (YouOnly-Look-Once) object detection method. It outperforms existing networks in terms of execution time and detection performance. The experimental results show improved mAP (mean average precision) performance of the proposed method for object detection in remote sensing images. We thus propose a novel system for automatic object detection for high-resolution remote sensing images.

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来源期刊

IET Networks COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

5.00

自引率

0.00%

发文量

审稿时长

33 weeks

期刊介绍： IET Networks covers the fundamental developments and advancing methodologies to achieve higher performance, optimized and dependable future networks. IET Networks is particularly interested in new ideas and superior solutions to the known and arising technological development bottlenecks at all levels of networking such as topologies, protocols, routing, relaying and resource-allocation for more efficient and more reliable provision of network services. Topics include, but are not limited to: Network Architecture, Design and Planning, Network Protocol, Software, Analysis, Simulation and Experiment, Network Technologies, Applications and Services, Network Security, Operation and Management.