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CCHA YOLO for mycelium clamp connection (CC) and hyphae Autolysis(HA) detection under microscopy imaging and web deployment.

Authors :
Wu, Libin
Lin, Shaodan
Jin, Wensong
Weng, Haiyong
Xu, Jinchai
Zhang, LinTong
Xu, Yawen
Xiang, Lirong
Sun, Shujing
Ye, Dapeng
Source :
Microchemical Journal. Jun2024, Vol. 201, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

[Display omitted] • A microscopic mycelium dataset was curated, representing the first of its kind for edible fungal mycelium quality determination. • An improved model CCHA YOLO was proposed for multi-scale object detection, which has been optimized through modifications to the backbone (C3Swin), head (CBAM), and post-processing methods (NWD-NMS and NWD-loss). • Validation and ablation experiments have demonstrated that CCHA YOLO outperforms other SOTA detection models in mycelium vitality detection. • CCHA YOLO has been applied in a web-based edge deployment, facilitating practical mycelium microscopic detection. Microscopic examination is commonly employed to assess edible fungal mycelium vitality. However, this method can become time-intensive when evaluating a substantial volume of hyphae samples, which implies an urgent need to develop an accurate and automatic determination method. The challenges of mycelium detection come mostly from the multi-scale target detection under various magnifications. In this study, microscopic images of 10 edible fungi strains under different magnification scales or stain colors were collected to create a dataset. An improved multi-scale object detection model for mycelium vitality detection, CCHA YOLO, was proposed by enhancing the Backbone via combining Yolov8m and Swin Transformer (SwinT). Meanwhile, the Convolutional Block Attention Module (CBAM) was introduced to the Head, as well as optimized post-processing techniques to further promote model performance. The results indicated that CCHA YOLO achieved a mAP 50:95 (mean average precision) of 89.02 % with a computational load of 98.61 GFLOPs. Additionally, it indicates a 16.67 % accuracy enhancement, needing only 11.3 more computational operations compared to the baseline YOLOv8m. In the meantime, CCHA YOLO was deployed on the web-based edge to facilitate the detection of microscopic images, highlighting the practical applicability of CCHA YOLO in determining mycelium vitality. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0026265X
Volume :
201
Database :
Academic Search Index
Journal :
Microchemical Journal
Publication Type :
Academic Journal
Accession number :
177351822
Full Text :
https://doi.org/10.1016/j.microc.2024.110483