Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Detection of microalgae objects based on the Improved YOLOv3 model
Mengying Cao, Junsheng Wang, Yantong Chen, et al.
Environmental Science Processes & Impacts (2021) Vol. 23, Iss. 10, pp. 1516-1530
Closed Access | Times Cited: 17

Showing 17 citing articles:

Smartphone-integrated multi-color ratiometric fluorescence portable optical device based on deep learning for visual monitoring of Cu2+ and thiram
Zhiwei Lu, Maoting Chen, Mengjiao Li, et al.
Chemical Engineering Journal (2022) Vol. 439, pp. 135686-135686
Closed Access | Times Cited: 79
Bag of Strategies Set New State-of-the-art for Algae Object Detectors
Zhiqiang Yang, Haiming Wen, Zihan Wei, et al.
(2022), pp. 1-9
Closed Access | Times Cited: 55
Machine learning for microalgae detection and utilization
Hongwei Ning, Rui Li, Teng Zhou
Frontiers in Marine Science (2022) Vol. 9
Open Access | Times Cited: 38
FE-YOLO: An Efficient Deep Learning Model Based on Feature-Enhanced YOLOv7 for Microalgae Identification and Detection
Gege Ding, Yuhang Shi, Zhenquan Liu, et al.
Biomimetics (2025) Vol. 10, Iss. 1, pp. 62-62
Open Access
Detecting living microalgae in ship ballast water based on stained microscopic images and deep learning
Ming Xie, Zhichen Liu, Liu Yu
Marine Pollution Bulletin (2025) Vol. 213, pp. 117608-117608
Closed Access
Advancing Algal Biofuel Production through Data-Driven Insights: A Comprehensive Review of Machine Learning Applications
Omole Olakunle, Chukwuma C. Ogbaga, Jude A. Okolie, et al.
Computers & Chemical Engineering (2025), pp. 109049-109049
Closed Access
A critical review of machine-learning for “multi-omics” marine metabolite datasets
Janani Manochkumar, Aswani Kumar Cherukuri, Raju Suresh Kumar, et al.
Computers in Biology and Medicine (2023) Vol. 165, pp. 107425-107425
Closed Access | Times Cited: 13
Vision meets algae: A novel way for microalgae recognization and health monitor
Shizheng Zhou, Juntao Jiang, Xiaohan Hong, et al.
Frontiers in Marine Science (2023) Vol. 10
Open Access | Times Cited: 12
An improved algae-YOLO model based on deep learning for object detection of ocean microalgae considering aquacultural lightweight deployment
Dan Liu, Pengqi Wang, Yuan Cheng, et al.
Frontiers in Marine Science (2022) Vol. 9
Open Access | Times Cited: 15
YOLOx model-based object detection for microalgal bioprocess
Huchao Yan, Xinggan Peng, Cheng Chen, et al.
Algal Research (2023) Vol. 74, pp. 103178-103178
Closed Access | Times Cited: 4
MAgic: A Morphable Attention Based Algal Tiny Object Detection Model
Shuai Yuan, Ningkang Peng, Ziyan Shi, et al.
(2023)
Closed Access | Times Cited: 2
Detection of algal tiny objects based on morphological features
Shuai Yuan, Ningkang Peng, Ziyan Shi, et al.
Concurrency and Computation Practice and Experience (2024) Vol. 36, Iss. 17
Closed Access
An artificial intelligence handheld sensor for direct reading of nickel ion and ethylenediaminetetraacetic acid in food samples using ratiometric fluorescence cellulose paper microfluidic chip
Liru Yan, Bianxiang Zhang, Wei Zhou, et al.
International Journal of Biological Macromolecules (2024) Vol. 279, pp. 135083-135083
Closed Access
Contemporary Methodologies for Identifying and Categorizing Microalgae: A Comprehensive Review and Future Perspectives
WEI HAN POK, Md Faiz Ahmad
ELEKTRIKA- Journal of Electrical Engineering (2024) Vol. 23, Iss. 2, pp. 10-22
Open Access
GIFF-AlgaeDet: An effective and lightweight deep learning method based on Global Information and Feature Fusion for microalgae detection
Yanjuan Wang, Zhenquan Liu, Jiayue Liu, et al.
Algal Research (2024), pp. 103815-103815
Closed Access
MRST-YOLO: A Novel Microalgae Detection Method Based On YOLOv5s and Mixed Residual Swin Transformer
Yantong Chen, Yang Liu, Yanyan Zhang, et al.
Research Square (Research Square) (2023)
Open Access
Accurate detection of microalgae in ship ballast water: An innovative computer vision strategy
Yantong Chen, Yang Liu, Jialiang Wang, et al.
Ecological Informatics (2023) Vol. 78, pp. 102311-102311
Closed Access
Page 1

Scroll to top