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全文摘要次数: 232 全文下载次数: 317
引用本文:

DOI:

10.11834/jrs.20210005

收稿日期:

2020-01-08

修改日期:

2020-04-13

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基于Poly-FPN神经网络模型的化工厂智能检测
昝露洋1, 李柏鹏1, 卢凯旋1, 陈正超1, 张兵2
1.中国科学院 空天信息创新研究院 航空遥感中心;2.中国科学院 空天信息创新研究院 数字地球重点实验室
摘要:

化工厂安全事故频发,国家对化工企业进行大力度整治排查。相比较小范围、低效率的人工排查而言,卫星遥感可以在大范围内高效的对化工厂进行监测,对我国化工行业生产安全监管、事故防范等极为重要。由于化工厂形状不规则、语义信息强,分布散乱,导致现有检测模型精度低,难以用于应用。本文提出Poly-FPN目标检测模型,该模型采用特征金字塔结构,在充分考虑模型感受野、多尺度融合等因素的前提下,提高了对复杂语义目标的处理能力,同时通过本文提出的任意四边形检测头替代传统的正矩形检测头,实现了不规则目标的精准检测。最后,本研究基于Poly-FPN对长江下游大范围地区进行预测,实现高精度的化工厂检测工作。

Intelligent Detection of Chemical Plants Based on Poly-FPN Neural Network
Abstract:

Chemical plant safety accidents are frequent, the government has carried out a major crackdown on chemical enterprises. Compared with manual investigation with a small range and low efficiency, satellite remote sensing can efficiently monitor chemical plants in a large range, which is of great importance for production safety supervision and accident prevention in China"s chemical industry. Due to the irregular shape, strong semantic information and scattered distribution of chemical plants, the existing detection model has low precision and is difficult to be applied. In this paper, we proposed Poly-FPN object detection model, this model adopts the feature pyramid structure, improve the processing capacity of the complicated semantic target, at the same time through the proposed arbitrary quadrilateral detection head alternatives to the traditional rectangular head, realizing the objective of the irregular accurate detection. Finally, based on poly-fpn, a large area of the lower reaches of the Yangtze river was predicted, and the high-precision detection of chemical plants was realized.

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