基于YOLOv8的多任务细菌菌落检测与分类及其面向资源受限部署的边缘优化
Multi-Task Bacterial Colony Detection and Classification Using YOLOv8 with Edge Optimization for Resource-Constrained Deployment
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中文总结 AI 辅助
本研究提出基于YOLOv8的多任务框架，在AGAR数据集上实现菌落计数与分类，准确率达98%以上，并通过剪枝与量化优化，在树莓派上实现高效部署。
中文摘要 AI 辅助
在微生物学中，细菌菌落的计数与分类是至关重要但劳动密集的任务，尤其在菌落密集的培养皿上，人工操作容易出现错误。本研究提出了一种多任务深度学习框架，该框架基于自动识别注释细菌（AGAR）数据集（包含18,000张图像；其中9,202张用于训练，3,067张用于测试）进行训练，以实现菌落形成单位（CFU）的自动计数和物种分类。采用全局回归的定制多任务CNN作为基线模型，但在菌落聚集环境中，由于缺乏空间定位能力，其性能表现有限。为解决这一问题，本研究采用了YOLOv8目标检测架构，并输入高分辨率1024x1024图像，从而实现实例级别的菌落检测和标签分配。该模型达到了98.13%的分类准确率和98.27%的计数准确率（在10个菌落的误差范围内），展现了强大的预测能力。为弥合模型性能与实际部署之间的差距，通过非结构化和结构化剪枝、ONNX转换以及降低精度推理（FP32、FP16、INT8）对训练好的模型进行了优化。在树莓派4B上，ONNX FP32和FP16版本在推理速度（约6.4秒）和准确率（MAE约2.20）之间提供了最佳平衡。非结构化剪枝保持了预测准确率（MAE约2.01），但未带来运行时间上的提升；而结构化剪枝则导致准确率显著下降（MAE约6.3），揭示了实例级菌落检测对架构压缩的敏感性。这些发现为在资源受限的实验室部署中选择优化策略提供了实用指导。
英文摘要
Manual counting and classification of bacterial colonies are critical yet labor-intensive tasks in microbiology, prone to human error particularly on densely populated plates. This work proposes a multi-task deep learning framework trained on the Annotated Germs for Automated Recognition (AGAR) dataset (18,000 images; 9,202 training / 3,067 testing) to automate Colony Forming Unit (CFU) enumeration and species classification. A custom multi-task CNN employing global regression served as the baseline, but demonstrated limited performance in clustered colony environments due to the absence of spatial localization. To address this, a YOLOv8 object detection architecture was adopted with high-resolution 1024x1024 inputs, enabling instance-level colony detection and label assignment. The model achieved a classification accuracy of 98.13% and a counting accuracy of 98.27% (within a 10-colony margin), demonstrating strong predictive capability. To bridge the gap between model performance and practical deployability, the trained model was optimized through unstructured and structured pruning, ONNX conversion, and reduced-precision inference (FP32, FP16, INT8). On a Raspberry Pi 4B, ONNX FP32 and FP16 variants offered the best balance between inference speed (~6.4s) and accuracy (MAE ~2.20). Unstructured pruning preserved predictive accuracy (MAE ~2.01) without runtime gains, while structured pruning resulted in significant accuracy degradation (MAE ~6.3), revealing the sensitivity of instance-level colony detection to architectural compression. These findings provide practical guidance for selecting optimization strategies in resource-constrained laboratory deployments.
