Recursive-YOLOv5 Network for Edible Mushroom Detection in Scenes With Vertical Stick Placement

In many rural edible mushroom growing sites, due to their growing environments and economic factors, growers are often unable to use industrial automation to pick edible mushrooms, resulting in losses of hundreds of millions of dollars each year due to missing the optimal picking cycles of edible mu...

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Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.40093-40108
Main Authors: Wei, Bohan, Zhang, Yao, Pu, Yufan, Sun, Yiliang, Zhang, Shihan, Lin, Hongyu, Zeng, Changfan, Zhao, Yahui, Wang, Kejun, Chen, Zhiyong
Format: Article
Language:English
Subjects:
Algorithms
ASPP
Cloud computing
Crops
Deep learning
Economic factors
Edible mushroom
Feature extraction
Machine learning
Mirrors
Mushrooms
Object detection
Object recognition
Target recognition
Training
YOLOv5
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Summary:In many rural edible mushroom growing sites, due to their growing environments and economic factors, growers are often unable to use industrial automation to pick edible mushrooms, resulting in losses of hundreds of millions of dollars each year due to missing the optimal picking cycles of edible mushrooms. For this reason, this project aimed to find an object detection algorithm that can be used with industrial cameras to help detect the growth statuses of edible mushrooms in real time and can be applied to future automatic picking machines. In this paper, based on the You Only Look Once version 5 (YOLOv5) network, by introducing the idea of recursion, remerging the first output with the convolutional layer of the backbone part of the network, introducing atrous spatial pyramid pooling (ASPP) instead of SPP (as in the original algorithm), utilizing the complete intersection over union (CIOU) and distance IOU (DIOU) instead of the generalized IOU (GIOU), and employing DIOU_nonmaximum suppression (NMS) instead of the original NMS algorithm, we propose a new deep learning network called Recursive-YOLOv5. This network can effectively identify 98% of edible mushrooms when dealing with large-resolution, small-target situations; this is a 12.87% improvement over the accuracy of YOLOv5X. Although the algorithm proposed in this paper utilizes almost double the parameters of the original network, with today's computing power and the rapid development of cloud computing, this sacrifice of computing power for accuracy is very cost-effective.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3165160