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Method for pests detecting in stored grain based on spectral residual saliency edge detection

التفاصيل البيبلوغرافية
العنوان: Method for pests detecting in stored grain based on spectral residual saliency edge detection
المؤلفون: Yao Qin, Yanli Wu, Qifu Wang, Suping Yu
المصدر: Grain & Oil Science and Technology, Vol 2, Iss 2, Pp 33-38 (2019)
بيانات النشر: KeAi Communications Co., Ltd., 2019.
سنة النشر: 2019
المجموعة: LCC:Agriculture
LCC:Food processing and manufacture
مصطلحات موضوعية: Agriculture, Food processing and manufacture, TP368-456
الوصف: Pests detecting is an important research subject in grain storage field. In the past decades, many edge detection methods have been applied to the edge detection of stored grain pests. Although some of them can realize the stored grain pests detecting, precision and robustness are not good enough. Spectral residual (SR) saliency edge detection defines the logarithmic spectrum of image as novelty part of the image information. The remaining spectrum is converted to the airspace to obtain edge detection results. SR algorithm is completely based on frequency domain processing. It not only can effectively simplify the target detection algorithm, but also can improve the effectiveness of target recognition. The experimental results show that the edge results of stored grain pests detected by SR method are effective and stable. Keywords: Stored grain pests, Saliency detection, Spectral residual (SR), Edge detection
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2590-2598
Relation: http://www.sciencedirect.com/science/article/pii/S259025981930010X; https://doaj.org/toc/2590-2598
DOI: 10.1016/j.gaost.2019.06.001
URL الوصول: https://doaj.org/article/8ac6498655c444a7bca166b3c8404f72
رقم الأكسشن: edsdoj.8ac6498655c444a7bca166b3c8404f72
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:25902598
DOI:10.1016/j.gaost.2019.06.001