شبکه عصبی عمیق مبتنی بر کالیبراسیون مجدد
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شبکه عصبی عمیق مبتنی بر کالیبراسیون مجدد

عنوان فارسی مقاله: MSARN: یک شبکه عصبی عمیق مبتنی بر یک مکانیسم کالیبراسیون مجدد سازگار برای تشخیص کشتی SAR (رادار روزنه مجازی) خودمختار و چند مقیاسی
عنوان انگلیسی مقاله: MSARN: A Deep Neural Network Based on an Adaptive Recalibration Mechanism for Multiscale and Arbitrary-Oriented SAR Ship Detection
مجله/کنفرانس: دسترسی – IEEE Access
رشته های تحصیلی مرتبط: مهندسی کامپیوتر، مهندسی فناوری اطلاعات
گرایش های تحصیلی مرتبط: هوش مصنوعی، شبکه های کامپیوتری
کلمات کلیدی فارسی: تشخیص کشتی، رادار روزنه مجازی، کالیبراسیون مجدد سازگار، شبکه عصبی
کلمات کلیدی انگلیسی: Ship detection, synthetic aperture radar (SAR), adaptive recalibration, neural network
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1109/ACCESS.2019.2951030
دانشگاه: Xi’an Institute of High-Technology, Xi’an 710025, China
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.641 در سال 2018
شاخص H_index: 56 در سال 2019
شاخص SJR: 0.609 در سال 2018
شناسه ISSN: 2169-3536
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 22
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13974
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


I. Introduction


II. Methods


III. Experiments


IV. Discussion


V. Conclusion


Authors


Figures


References

نمونه متن انگلیسی مقاله

Abstract


Ship detection plays an important role in synthetic aperture radar (SAR) image interpretation. However, there are still some difficulties in SAR ship detection. First, ships often have a large aspect ratio and arbitrary directionality in SAR images. Traditional detection algorithms can cause the detection area to be redundant, which makes it difficult to accurately locate the target in complex scenes. Second, ships in ports are often densely arranged, and the effective identification of densely arranged ships is complicated. Finally, ships in SAR images exist at a variety of scales due to the multiresolution imaging modes used and ship shape variations, which pose a considerable challenge for ship detection. To solve the above problems, we propose a multiscale adaptive recalibration network (MSARN) to detect multiscale and arbitrarily oriented ships in complex scenarios. The recalibration of the extracted multiscale features through global information increases the sensitivity of the network to the target angle, thereby increasing the accuracy of positioning. In particular, we designed a pyramid anchor and a loss function to match the rotated target. In addition, we modified the rotation non-maximum suppression (RNMS) method to solve the problem of the large overlap ratio of the detection box. The proposed model combines the positioning advantage of rotation detection with the speed advantage of a single-stage framework. Experiments show that based on the SAR rotation ship detection (SRSD) data set, the proposed algorithm has a faster detection speed and higher accuracy than some state-of-the-art methods.


Introduction


Synthetic aperture radar (SAR) has been widely used in civil remote sensing surveying and military reconnaissance due to its independence from solar illumination and ability to provide images in all-weather operating conditions [1]–[4]. In recent years, SARs such as TerraSAR-X, RADARSAT-2, and Sentinel-1 have rapidly developed, which has greatly promoted research on SAR image ship detection methods [5]–[7]. The constant false alarm rate (CFAR) and its various derivative algorithms are widely used in SAR image ship detection [8]–[10]. This type of algorithm is based on a statistical model of the contrast information, which can automatically adjust thresholds to suit different ocean backgrounds while maintaining the required performance. However, the algorithm modelling process is complicated, and ship detection in complex backgrounds cannot achieve the desired effect. A deep convolutional neural network (DCNN) can automatically learn the structural features of a target and have been rapidly developed in the field of computer vision. A series of object detection algorithms based on DCNN was proposed. The detection algorithms based on DCNN mainly include two types: two-stage detection algorithms, such as the Faster R-CNN [11], and single-stage detection algorithms, such as SSD [12], YOLO [13]–[15], and RFBNet [16].

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