Picking point identification and localization method based on swin-transformer for high-quality tea

IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-12-01 DOI:10.1016/j.jksuci.2024.102262
Zhiyao Pan, Jinan Gu, Wenbo Wang, Xinling Fang, Zilin Xia, Qihang Wang, Mengni Wang
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Abstract

In the nature scene, because of the high degree of similarity between the background and the tea buds, as well as the different growth postures of the tea buds, finding and precisely identifying the picking point is challenging. To solve these issues, this paper proposes a precise way to find the best picking point for tea buds by combining traditional algorithms with Swin-Transformer-based target detection and semantic segmentation algorithms, namely SORC-SFT. Firstly, an improved target detection algorithm, Swin-Oriented R-CNN (SORC), is used to realize the recognition of four types of high-quality tea. The mean Average Precision (mAP) of the four categories was 82.3% after replacing the feature fusion network FPN with PAFPN and adding the Coordinate Attention (CA) mechanism. Secondly, the corresponding segmentation mask of the four recognized categories is obtained by adding Semask, Feature Alignment Module (FAM), and Feature Selection Module (FSM) to the improved semantic segmentation algorithm Semask-Fa-Transformer (SFT). The mean Intersection over Union (mIoU) of the semantic segmentation algorithm for each category is 89.83%, 91.97%, 88.85%, and 89.68%, respectively. Finally, the morphology of different categories of tea buds is analyzed, and the traditional algorithm is used to realize the accurate localization of the identified tea buds. For the four tested categories, the proportion of correct samples in locating picking points is 96.18%, 91.28%, 93.85%, and 90.58%, respectively. The experimental results show that, out of all the algorithms, the proposed picking point identification and localization approach has the best performance and will make a strong contribution to the accurate identification of tea leaves during the intelligent picking process.
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在自然场景中,由于背景与茶芽的相似度较高,且茶芽的生长姿态各不相同,因此寻找并精确识别采摘点具有一定的挑战性。为了解决这些问题,本文通过将传统算法与基于斯文变换器的目标检测和语义分割算法(即 SORC-SFT)相结合,提出了一种精确寻找茶芽最佳采摘点的方法。首先,使用改进的目标检测算法 Swin-Oriented R-CNN (SORC) 实现对四种优质茶叶的识别。将特征融合网络 FPN 替换为 PAFPN 并加入坐标注意(CA)机制后,四类茶叶的平均精度(mAP)为 82.3%。其次,在改进的语义分割算法 Semask-Fa-Transformer(SFT)中加入 Semask、特征对齐模块(FAM)和特征选择模块(FSM),得到四个识别类别的相应分割掩码。每个类别的语义分割算法的平均交集大于联合率(mIoU)分别为 89.83%、91.97%、88.85% 和 89.68%。最后,对不同类别的茶芽进行形态分析,并利用传统算法实现对识别出的茶芽的精确定位。对于四个测试类别,采摘点定位的正确样本比例分别为 96.18%、91.28%、93.85% 和 90.58%。实验结果表明,在所有算法中,所提出的采摘点识别和定位方法性能最佳,将为智能采摘过程中茶叶的准确识别做出有力贡献。
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来源期刊
CiteScore
10.50
自引率
8.70%
发文量
656
审稿时长
29 days
期刊介绍: In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.
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