| 摘 要: 针对菌落计数问题,人工计数方法存在效率低、精度不高的问题。为了解决这些问题,提出了一种改进YOLOv5的模型,即YOLOES。该模型通过添加小目标检测层,并将Kmeans算法替换为Kmeans++算法,以更好地适应不同尺寸的目标;同时,采用Focal-EIoU损失函数解决难易样本的问题,引入了SPPCSPS(Spatial Pyramid Pooling Convolutional Spatial Pyramid Convolution)模块以增强特征表示能力,并在特征提取阶段引入了置换注意力机制。通过在大肠杆菌菌落数据集进行实验验证,结果显示相较于初始的YOLOv5模型,YOLOES的mAP@0.5提升了17.3百分点,表明YOLOES在菌落检测任务上具有更优越的性能。 |
| 关键词: YOLOv5;图像识别;Kmeans++;Focal-EIoU;SPPCSPS;置换注意力机制 |
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中图分类号: TP391
文献标识码: A
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| 基金项目: 浙江省高层次人才特殊支持计划(2021R52019) |
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| Research on Colony Counting Algorithm Based on Improved YOLOv5 |
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FAN Xiangyu, DAI Qi
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(College of Li f e Sciences and Medicine, Zhejiang SCI-TECH University, Hangzhou 310020, China)
1871541711@qq.com; daiqi@zstu.edu.cn
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| Abstract: Aiming at the low efficiency and low accuracy of manual colony counting, this paper proposes an improved model based on YOLOv5, named YOLOES. This model incorporates a small object detection layer and replaces the Kmeans algorithm with the Kmeans + + algorithm to better accommodate targets of various sizes. Additionally, it employs the Focal-EIoU loss function to tackle the problem of hard and easy samples, introduces the SPPCSPS ( Spatial Pyramid Pooling Convolutional Spatial Pyramid Convolution ) module to enhance feature representation capability, and integrates a permutation attention mechanism in the feature extraction phase. Experiments conducted on a dataset of Escherichia coli colonies indicate that YOLOES achieves a 17.3 percentage points improvement in mAP@0.5 compared to the original YOLOv5 model, demonstrating its superior performance in colony detection tasks. |
| Keywords: YOLOv5; image recognition; Kmeans++; Foca-l EIoU; SPPCSPS; permutation attention mechanisms |
