Efficient online detection device and method for cottonseed breakage based on Light-YOLO

被引:1
|
作者
Zhang, Hongzhou [1 ]
Li, Qingxu [2 ]
Luo, Zhenwei [1 ]
机构
[1] Tarim Univ, Coll Mech & Elect Engn, Alar, Peoples R China
[2] Anhui Univ Finance & Econ, Inst Cotton Engn, Bengbu, Peoples R China
来源
基金
美国国家科学基金会;
关键词
cottonseed; breakage; YOLOV8m; Light-YOLO; online detection;
D O I
10.3389/fpls.2024.1418224
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
High-quality cottonseed is essential for successful cotton production. The integrity of cottonseed hulls plays a pivotal role in fostering the germination and growth of cotton plants. Consequently, it is crucial to eliminate broken cottonseeds before the cotton planting process. Regrettably, there is a lack of rapid and cost-effective methods for detecting broken cottonseed at this critical stage. To address this issue, this study developed a dual-camera system for acquiring front and back images of multiple cottonseeds. Based on this system, we designed the hardware, software, and control systems required for the online detection of cottonseed breakage. Moreover, to enhance the performance of cottonseed breakage detection, we improved the backbone and YOLO head of YOLOV8m by incorporating MobileOne-block and GhostConv, resulting in Light-YOLO. Light-YOLO achieved detection metrics of 93.8% precision, 97.2% recall, 98.9% mAP50, and 96.1% accuracy for detecting cottonseed breakage, with a compact model size of 41.3 MB. In comparison, YOLOV8m reported metrics of 93.7% precision, 95.0% recall, 99.0% mAP50, and 95.2% accuracy, with a larger model size of 49.6 MB. To further validate the performance of the online detection device and Light-YOLO, this study conducted an online validation experiment, which resulted in a detection accuracy of 86.7% for cottonseed breakage information. The results demonstrate that Light-YOLO exhibits superior detection performance and faster speed compared to YOLOV8m, confirming the feasibility of the online detection technology proposed in this study. This technology provides an effective method for sorting broken cottonseeds.
引用
收藏
页数:18
相关论文
共 50 条
  • [1] Light-YOLO: A Lightweight and Efficient YOLO-Based Deep Learning Model for Mango Detection
    Zhong, Zhengyang
    Yun, Lijun
    Cheng, Feiyan
    Chen, Zaiqing
    Zhang, Chunjie
    AGRICULTURE-BASEL, 2024, 14 (01):
  • [2] Light-YOLO: A Study of a Lightweight YOLOv8n-Based Method for Underwater Fishing Net Detection
    Chen, Nuo
    Zhu, Jin
    Zheng, Linhan
    APPLIED SCIENCES-BASEL, 2024, 14 (15):
  • [3] Light-YOLO: a lightweight detection algorithm based on multi-scale feature enhancement for infrared small ship target
    Tang, Ji
    Hu, Xiao-Min
    Jeon, Sang-Woon
    Chen, Wei-Neng
    COMPLEX & INTELLIGENT SYSTEMS, 2025, 11 (02)
  • [4] Online Yarn Breakage Detection: A Reflection-Based Anomaly Detection Method
    Yan, Ning
    Zhu, Linlin
    Yang, Hongmai
    Li, Nana
    Zhang, Xiaodong
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2021, 70
  • [5] ONLINE TOOL BREAKAGE DETECTION IN TURNING - A MULTISENSOR METHOD
    COLGAN, J
    CHIN, H
    DANAI, K
    HAYASHI, SR
    JOURNAL OF ENGINEERING FOR INDUSTRY-TRANSACTIONS OF THE ASME, 1994, 116 (01): : 117 - 123
  • [6] Efficient and Accurate Beach Litter Detection Method Based on QSB-YOLO
    Zhu, Hanling
    Zhu, Daoheng
    Qin, Xue
    Guo, Fawang
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2023, 14 (07) : 311 - 322
  • [7] Mask Detection Based On Efficient-YOLO
    Li, Yi
    Yan, Jiayuan
    Hu, Bin
    2021 PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE (CCC), 2021, : 4056 - 4061
  • [8] Online Rail Fastener Detection Based on YOLO Network
    Li, Jun
    Qiu, Xinyi
    Wei, Yifei
    Song, Mei
    Wang, Xiaojun
    Computers, Materials and Continua, 2022, 72 (03): : 5955 - 5967
  • [9] Online Rail Fastener Detection Based on YOLO Network
    Li, Jun
    Qiu, Xinyi
    Wei, Yifei
    Song, Mei
    Wang, Xiaojun
    CMC-COMPUTERS MATERIALS & CONTINUA, 2022, 72 (03): : 5955 - 5967
  • [10] Detection Method of Double Side Breakage of Population Cotton Seed Based on Improved YOLO v4
    Wang, Qiaohua
    Gu, Wei
    Cai, Peizhong
    Zhang, Hongzhou
    Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery, 2022, 53 (01): : 389 - 397