Remote sensing ship recognition technology is one of the important research areas for achieving ocean security monitoring. However, maritime ship targets features such as multi-scale, arbitrary direction, and dense arrangement, and their imaging is often influenced by factors such as sea fog and sea surface background. It is challenging for fast and accurate detection of remote sensing ships. We propose a new ship detection algorithm YOLO-Remote Sensing Ship Detection (YOLO-RSSD) based on YOLOv5, which effectively improves the accuracy of ship detection while ensuring detection speed. It is attributed to our proposed four optimization measures in this model: First, the K-means++ algorithm is introduced in the data preprocessing part to improve the clustering effect and obtain candidate box sizes suitable for multi-scale ship dataset. Then, an improved BiFPN structure is embeded in the feature fusion part to achieve cross-layer multi-scale weighted feature fusion. Thirdly, the original bounding box regression loss function is replaced with the EIoU loss, which is effective for accelerating the convergence and improving the regression accuracy of predicted boxes. Finally, a channel attention mechanism is introduced in the convolutional unit to enhance the model's ability to capture ship features. Experimental results show that the YOLO-RSSD model achieves a detection accuracy of 96.1 $\%$ for remote sensing ships, which is 4.3 $\%$ higher than the original YOLOv5 network. In addition, YOLO-RSSD performs good robustness and generalization ability. This means that our method has high practical value and provides a new solution for the analysis and application of remote sensing images.

A compact and label-free biosensor based on coreless-fiber-coupled microcavity is proposed and experimentally demonstrated for bovine serum albumin (BSA) detection. The microcavity is fabricated in a coreless fiber by on-line fiber cutting-welding micromachining. Experimental results show that the BSA samples with different concentration and refractive index ranging from 1.3344 to 1.3349 are clearly discriminated by spectral characteristics of the coreless-fiber-coupled microcavity, with a sensitivity of 1.602 nm/(mg/ml). The device presents superiorities such as miniature size, standardized production control, reliable quality, and cost effective, which makes it a good candidate for the detection of low-concentration micro-samples analytes in the fields of life sciences, biological engineering, and biopharmaceutical industry.

Owing to their unique structural design and strong light-matter interaction, microstructure optical fibers (MOFs) emerge as a promising platform for optical sensing. When MOF-based sensors are perturbed, the acquired spectrum data are scrambled that significantly limits their practical application. To overcome this challenge, a functionalized MOF enabled by deep learning-based data analytics is proposed and experimentally demonstrated to realize temperature sensing in the presence of external perturbations. Under different temperature states, the trained deep learning model can effectively extract features from the collected signal data and achieve highly accurate recognition of complex speckle patterns. Experimental results show that the speckle recognition accuracy of the deep learning model reaches 98.64% and 100% in the presence and absence of external perturbations, respectively. The approach herein can be extended to other MOF-based sensing systems with regular or irregular speckle patterns for detecting various measurands. And moreover, the MOF integrated with an optical neural network possesses several desirable features such as high recognition accuracy, anti-interference capability and simplified configuration, showing great potential toward realizing the smart monitoring applications in the aspects of healthcare, industry, and environmental engineering..

A fiber-optic sensor for simultaneously measurement of temperature and salinity of seawater is proposed and demonstrated in this paper. The sensor is fabricated by self-growing a size-controllable UV-photopolymer micro-cone on the fiber facet. The optimal processing process and micro-cone size are obtained through theoretical analysis. This sensor is featured for simple fabrication, low cost and high efficiency, which provides a new lab-on-fiber scheme. And it was experimentally verified that the sensor shows fast responses to both of temperature and salinity of seawater with a high temperature sensitivity of −0.177 nm/°C between 25 °C and 100 °C and a high salinity sensitivity of −0.848 nm/‰ for the salinity range of 3‰ to 12‰. By using the transmission matrix method, simultaneous measurement of temperature and salinity can be realized and the accuracy of sensor detection is verified as well, which enables real-time long-term monitoring of ocean temperature and salinity.