With the rapid development of earth observation technology, high-resolution synthetic aperture radar (HR SAR) imaging satellites could provide more observational information for maritime surveillance. However, there are still some problems to detect ship targets in HR SAR images due to the complex surroundings, targets defocusing, and diversity of the scales. In this article, an anchor-free method is proposed for ship target detection in HR SAR images. First, fully convolutional one-stage object detection (FCOS) as the base network is applied to detect ship targets, achieving better detection performance through pixel-bypixel prediction of the image. Second, the category-position (CP) module is proposed to optimize the position regression branch features in the FCOS network. This module can improve target positioning performance in complex scenes by generating guidance vector from the classification branch features. At the same time, target classification and boundary box regression methods are redesigned to shield the adverse effects of fuzzy areas in the network training. Finally, to evaluate the effectiveness of CP-FCOS, extensive experiments are conducted on HRSID, SSDD, IEEE 2020 Gaofen Challenge SAR dataset, and two complex largescene HR SAR images. The experimental results show that our method can obtain encouraging detection performance compared with Faster-RCNN, RetinaNet, and FCOS. Remarkably, the proposed method was applied to SAR ship detection in the 2020 Gaofen Challenge. Our team ranked first among 292 teams in the preliminary contest and won seventh place in the final match.
A novel fiber Michelson interferometer (FMI) based on parallel dual polarization maintaining fiber Sagnac interferometers (PMF-SIs) is proposed and experimentally demonstrated for temperature sensing. The free spectral range (FSR) difference of dual PMF-SIs determines the FSR of envelope and sensitivity of the sensor. The temperature sensitivity of parallel dual PMF-SIs is greatly enhanced by the Vernier effect. Experimental results show that the temperature sensitivity of the proposed sensor is improved from −1.646 nm/°C (single PMF-SI) to 78.984 nm/°C (parallel dual PMF-SIs), with a magnification factor of 47.99, and the temperature resolution is improved from ±0.03037°C to ±0.00063°C by optimizing the FSR difference between the two PMF-SIs. Our proposed ultrasensitive temperature sensor is with easy fabrication, low cost and simple configuration which can be implemented for various real applications that need high precision temperature measurement.
An optical fiber coupler is a simple and fundamental component for fiber optic technologies that works by reducing the fiber diameter to hundred nanometers or several micrometers. The microfiber coupler (MFC) has regained interest in optical fiber sensing in recent years. The subwavelength diameter rationales vast refractive index (RI) contrast between microfiber “core” and surrounding “cladding”, a large portion of energy transmits in the form of an evanescent wave over the fiber surface that determines the MFC ultrasensitive to local environmental changes. Consequently, MFC has the potential to develop as a sensor. With the merits of easy fabrication, low cost and compact size, numerous researches have been carried out on different microfiber coupler configurations for various sensing applications, such as refractive index (RI), temperature, humidity, magnetic field, gas, biomolecule, and so on. In this manuscript, the fabrication and operation principle of an MFC are elaborated and recent advances of MFC-based sensors for scientific and technological applications are comprehensively reviewed.
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