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Spatial-channel transformer network based on mask-RCNN for efficient mushroom instance segmentation

文献类型： 外文期刊

作者： Wang, Jiaoling 1 ; Song, Weidong 2 ; Zheng, Wengang 3 ; Feng, Qingchun 3 ; Wang, Mingfei 3 ; Zhao, Chunjiang 1 ;

作者机构： 1.Northwest Agr & Forestry Univ, Xian 712199, Peoples R China

2.Minist Agr & Rural Affairs, Nanjing Inst Agr Mechanizat, Nanjing 210014, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Technol Res Ctr, Beijing 100097, Peoples R China

4.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Zhejiang Prov Key Lab Agr Intelligent Equipment &, Hangzhou 310058, Peoples R China

关键词： edible mushrooms; picking; instance segmentation; deep learning; algorithm

期刊名称：INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING （ 影响因子：2.2； 五年影响因子：2.4 ）

ISSN： 1934-6344

年卷期： 2024 年 17 卷 4 期

页码：

收录情况： SCI

摘要： Edible mushrooms are rich in nutrients; however, harvesting mainly relies on manual labor. Coarse localization of each mushroom is necessary to enable a robotic arm to accurately pick edible mushrooms. Previous studies used detection algorithms that did not consider mushroom pixel-level information. When these algorithms are combined with a depth map, the information is lost. Moreover, in instance segmentation algorithms, convolutional neural network (CNN)-based methods are lightweight, and the extracted features are not correlated. To guarantee real-time location detection and improve the accuracy of mushroom segmentation, this study proposed a new spatial-channel transformer network model based on Mask-CNN (SCTMask-RCNN). The fusion of Mask-RCNN with the self-attention mechanism extracts the global correlation outcomes of image features from the channel and spatial dimensions. Subsequently, Mask-RCNN was used to maintain a lightweight structure and extract local features using a spatial pooling pyramidal structure to achieve multiscale local feature fusion and improve detection accuracy. The results showed that the SCT-Mask-RCNN method achieved a segmentation accuracy of 0.750 on segm_Precision_mAP and detection accuracy of 0.638 on Bbox_Precision_mAP. Compared to existing methods, the proposed method improved the accuracy of the evaluation metrics Bbox_Precision_mAP and segm_Precision_mAP by over 2% and 5%, respectively.

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