Bacteria colony detection methods based on Few-shot learning  

10:00am - 11:00am
ECE meeting room 2515-2516 (2/F via Lifts 25/26)

Bacterial colony counting is a time consuming but important task for many fields, such as food quality testing and pathogen detection, which own the high demand for accurate on-site testing. However, bacterial colonies are often overlapped, adherent with each other, and difficult to precisely process by traditional algorithms. The development of deep learning has brought new possibilities for bacterial colony counting, but deep learning networks usually require a large amount of training data and highly configured test equipment. The culture and annotation time of bacteria are costly, and professional deep learning workstations are too expensive and large to meet portable requirements. To solve these problems, we propose a lightweight improved YOLOv3 network based on the few-shot learning strategy, which is able to accomplish high detection accuracy with only five raw images and be deployed on a low-cost edge device. Compared with the traditional methods, our method improved the average accuracy from 64.3% to 97.4% and decreased the False Negative Rate from 32.1% to 1.5%. Our method could greatly improve the detection accuracy, realize the portability for on-site testing, and significantly save the cost of data collection and annotation over 80%, which brings more potential for bacterial colony counting.

讲者/ 表演者:
Dr. Beini Zhang
Research Assistant Professor, Quantum AI Center, the University of Hong Kong

Zhang Beini received the B.S. degree in Electronic Information Engineering from Chongqing University in 2018, the M.S. degree in Electrical Engineering from the Hong Kong University of Science and Technology in 2019, and the Ph.D. degree in Advanced Materials from the Hong Kong University of Science and Technology in 2022. She is currently working as a research assistant professor at the University of Hong Kong. Her main research interests include small sample learning, data augmentation, image recognition, and deep neural network-based image anti-noise processing.

语言
英文
适合对象
教职员
研究生
主办单位
电子及计算器工程学系
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