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Colony-YOLO: A Lightweight Micro-Colony Detection Network Based on Improved YOLOv8n

文献类型： 外文期刊

作者： Wang, Meihua 1 ; Luo, Junhui 1 ; Lin, Kai 1 ; Chen, Yuankai 1 ; Huang, Xinpeng 2 ; Liu, Jiping 2 ; Wang, Anbang 1 ; Xiao, Deqin 1 ;

作者机构： 1.South China Agr Univ, Coll Math & Informat, Guangzhou 510642, Peoples R China

2.South China Agr Univ, Coll Anim Sci, Guangdong Prov Key Lab Agroanim Genom & Mol Breed, Guangzhou 510642, Peoples R China

3.South China Agr Univ, Key Lab Smart Agr Technol Trop South China, Minist Agr & Rural Affairs, Guangzhou 510642, Peoples R China

关键词： colony detection; mulberry bacterial blight; YOLOv8; StarNet; attention mechanism; loss function

期刊名称：MICROORGANISMS （ 影响因子：4.2； 五年影响因子：4.6 ）

ISSN：

年卷期： 2025 年 13 卷 7 期

页码：

收录情况： SCI

摘要： The detection of colony-forming units (CFUs) is a time-consuming but essential task in mulberry bacterial blight research. To overcome the problem of inaccurate small-target detection and high computational consumption in mulberry bacterial blight colony detection task, a mulberry bacterial blight colony dataset (MBCD) consisting of 310 images and 23,524 colonies is presented. Based on the MBCD, a colony detection model named Colony-YOLO is proposed. Firstly, the lightweight backbone network StarNet is employed, aiming to enhance feature extraction capabilities while reducing computational complexity. Next, C2f-MLCA is designed by embedding MLCA (Mixed Local Channel Attention) into the C2f module of YOLOv8 to integrate local and global feature information, thereby enhancing feature representation capabilities. Furthermore, the Shape-IoU loss function is implemented to prioritize geometric consistency between predicted and ground truth bounding boxes. Experiment results show that the Colony-YOLO achieved an mAP of 96.1% on MBCDs, which is 4.8% higher than the baseline YOLOv8n, with FLOPs and Params reduced by 1.8 G and 0.8 M, respectively. Comprehensive evaluations demonstrate that our method excels in detection accuracy while maintaining lower complexity, making it effective for colony detection in practical applications.

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