基于Mask-RCNN的复杂背景下多目标叶片的分割和识别

doi:10.3969/j.issn.1004-1524.2020.11.17

浙江农业学报 ›› 2020, Vol. 32 ›› Issue (11): 2059-2066.DOI: 10.3969/j.issn.1004-1524.2020.11.17

基于Mask-RCNN的复杂背景下多目标叶片的分割和识别

钟伟镇, 刘鑫磊, 杨坤龙, 李丰果^*

华南师范大学物理与电信工程学院/物理国家级实验教学示范中心,广东广州 510006

收稿日期:2020-06-12 出版日期:2020-11-25 发布日期:2020-12-02
通讯作者: * 李丰果,E-mail: ganguli@126.com
作者简介:钟伟镇(1996—),男,广东揭阳人,硕士,研究方向为图像处理。E-mail: verzin@qq.com
基金资助:
国家自然科学基金(11575064); 广东省自然科学基金(2016A030313433)

Research on multi-target leaf segmentation and recognition algorithm under complex background based on Mask-RCNN

ZHONG Weizhen, LIU Xinlei, YANG Kunlong, LI Fengguo^*

School of Physics and Telecommunication Engineering, National Demonstration Center for Experimental Physics Education, South China Normal University, Guangzhou 510006, China

Received:2020-06-12 Online:2020-11-25 Published:2020-12-02

摘要/Abstract

摘要： 在基于叶片图像进行植物识别和生长状态监控时,植物目标叶片的准确分割和识别是前提和基础,但复杂背景给叶片的分割和识别带来了极大的挑战。本研究提出基于Mask-RCNN深度学习网络分割和识别复杂背景下多目标叶片的算法,共拍摄自然生长状态下常见的植物叶片图像7 357张,标注3 000张作为训练数据库,这3 000张图像共包含4种植物,分别为孔雀竹芋(Calathea makoyana)、珊瑚树(Viburnum odoratissinum)、洋常春藤(Hedera helix L.)和黄花羊蹄甲(Bauhinia tomentosa)。选择这4种植物的80个测试样本图像进行分割、识别与错分率分析。结果表明:Mask-RCNN深度学习网络对这4种植物的识别效果良好,未出现误识别的情况;分割的平均图像错分率为0.93%,最大值不超过2.49%,即分割准确率达97.51%;同时该算法具有强大的迁移能力。

关键词: 叶片识别, Mask-RCNN, 错分率, 准确率

Abstract: The accurate segmentation and recognition of plant target leaves is the premise and foundation for the plant recognition and growth state monitoring based on leaf images, but the complex background brings great challenges to the segmentation and recognition of leaves. In this study, an algorithm based on the Mask-RCNN deep learning network to segment and identify multi-target leaves in the complex background was proposed. A total of 7 357 images of common plant leaves in the nature were taken, and 3 000 images of four plant leaves were labeled as training databases. The 3 000 images contained four species of plants: Calathea makoyana, Viburnum odoratissinum, Hedera helix L. and Bauhinia tomentosa. 80 sample images of the four plant species were selected for the segmentation, identification and misclassification rate analysis. The results showed that the Mask-RCNN deep learning network had a good recognition effect for the four plants and there was no false recognition. The average segmentation error rate was 0.93%, with the maximum value not exceeding 2.49%, which meant the segmentation accuracy was 97.51%. At the same time, the algorithm had strong migration ability.

Key words: leaf recognition, Mask-RCNN, error rate, segmentation accuracy

中图分类号:

TP391.41

钟伟镇, 刘鑫磊, 杨坤龙, 李丰果. 基于Mask-RCNN的复杂背景下多目标叶片的分割和识别[J]. 浙江农业学报, 2020, 32(11): 2059-2066.

ZHONG Weizhen, LIU Xinlei, YANG Kunlong, LI Fengguo. Research on multi-target leaf segmentation and recognition algorithm under complex background based on Mask-RCNN[J]. , 2020, 32(11): 2059-2066.

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基于Mask-RCNN的复杂背景下多目标叶片的分割和识别

Research on multi-target leaf segmentation and recognition algorithm under complex background based on Mask-RCNN

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