The ocean connects all continents and is an important space for human activities. Ship detection with electro-optical images has shown great potential due to the abundant imaging spectrum and, hence, strongly supports human activities in the ocean. A suitable imaging spectrum can obtain effective images in complex marine environments, which is the premise of ship detection. This paper provides an overview of ship detection methods with electro-optical images in marine environments. Ship detection methods with sea–sky backgrounds include traditional and deep learning methods. Traditional ship detection methods comprise the following steps: preprocessing, sea–sky line (SSL) detection, region of interest (ROI) extraction, and identification. The use of deep learning is promising in ship detection; however, it requires a large amount of labeled data to build a robust model, and its targeted optimization for ship detection in marine environments is not sufficient.

Infrared ship detection is of great significance due to its broad applicability in maritime surveillance, traffic safety and security. Multiple infrared sensors with different spectral sensitivity provide enhanced sensing capabilities, facilitating ship detection in complex environments. Nevertheless, current researches lack discussion and exploration of infrared imagers in different spectral ranges for marine objects detection. Furthermore, for unmanned marine vehicles (UMVs), e.g., unmanned surface vehicles (USVs) and unmanned ship (USs), detection and perception are usually performed in embedded devices with limited memory and computation resource, which makes traditional convolutional neural network (CNN)-based detection methods struggle to leverage their advantages. Aimed at the task of sea surface object detection on USVs, this paper provides lightweight CNNs with high inference speed that can be deployed on embedded devices. It also discusses the advantages and disadvantages of using different sensors in marine object detection, providing a reference for the perception and decision-making modules of USVs. The proposed method can detect ships in short-wave infrared (SWIR), long-wave infrared (LWIR) and fused images with high-performance and high-inference speed on an embedded device. Specifically, the backbone is built from bottleneck depth-separable convolution with residuals. Generating redundant feature maps by using cheap linear operation in neck and head networks. The learning and representation capacities of the network are promoted by introducing the channel and spatial attention, redesigning the sizes of anchor boxes. Comparative experiments are conducted on the infrared ship dataset that we have released which contains SWIR, LWIR and the fused images. The results indicate that the proposed method can achieve high accuracy but with fewer parameters, and the inference speed is nearly 60 frames per second (FPS) on an embedded device.

High detection efficiency appears to be associated with a high afterpulse probability for InP-based single-photon avalanche diodes. In this paper, we present a new hybrid quenching technique that combines the advantages of both fast active quenching and high-frequency gated-passive quenching, with the aim of suppressing higher-order afterpulsing effects. Our results showed that the hybrid quenching method contributed to a 10% to 85% reduction of afterpulses with a gate-free detection efficiency of 4% to 10% at 1.06 $\mathsf{\mu }$m, with 40 ns dead time, compared with the counter-based hold-off method. With the improvement of the afterpulsing performance of high-frequency gated single-photon detectors, especially at relatively high average detection efficiencies with wide gate widths, the proposed method enables their use as high-performance free-running detectors.

In this paper, a modified infrared and visible image registration method based on contour feature is proposed. Our method firstly extracts the feature contour and eliminates sparkling waves contour of the sea surface, determines the main direction of the contour based on the contour image, then uses the improved Scale Invariant Feature Transform (SIFT) method as the feature point to construct the descriptor, completes the registration of the two images. 30 sets of infrared and visible-band vessels images were selected for registration experiments. Compared with previous reports, the experimental results showed that the proportion of effective feature points detected by this method can reach 70%, and the average number of effective feature points detected by proposed method can reach 196 in visible band image and 279 in infrared image. The running time was 5.3599s, shortened by 25% compared with previous reports, and the average Root Mean Square Error (RMSE) value was 2.3566, smaller by 75% compared with previous reports. An effective registration method is provided, which can be used for infrared and visible image processing and comprehensive utilization of information in marine scenes.

Optical projection tomography (OPT) is an advanced three-dimensional (3D) imaging technology, which uses the filtered backprojection (FBP) algorithm to recover the 3D volume with sufficient number of projections. As to in vivo imaging, it is urgent to reduce the number of projections because the acquisition time could be minimized. However, reconstructing from undersampled OPT data can lead to artifacts and the decline in image quality. The simultaneous iterative reconstruction technique (SIRT) is introduced to remove artifacts and improve the image quality. The image qualities reconstructed from FBP and SIRT separately are compared, and the structural similarity and peak signal-to-noise ratio are calculated. Through simulated phantoms and OPT data of in vivo zebrafish embryo, SIRT consistently outperforms FBP in terms of reduced artifacts and enhanced image contrast especially when the projection numbers are reduced. SIRT method can provide high-quality reconstruction with 50 or fewer projections, thereby significantly reducing the minimum acquisition time and light dose while maintaining reconstruction quality. Through optimization and GPU acceleration, the SIRT algorithm can converge faster so as to reduce the image processing time. To our knowledge, this is the first time one SIRT algorithm is used for reconstruction of sparse OPT data. The experimental results show that SIRT algorithm outperform FBP especially when the number of projections is reduced. In addition, SIRT performs better in the preservation of vascular signal, which is significant for the monitoring of angiogenesis.

A method for maintaining a fixed phase relationship between the driving signal of acousto-optic modulator (AOM) and the original mode-locked seed laser is proposed and realized, which stabilizes the amplitude of diffracted signal output from the AOM for subsequent amplification. A field-programmable gate array (FPGA), combined with external summing amplifiers, is used to directly synthesize high-timing-precision driving signals that are synchronized with the seed laser pulses, and the accuracy of signal timing control reaches 160 ps. Using this driver, the standard deviation of the diffracted signal output from the AOM is significantly decreased from 0.52% to 0.18%. This pulse-picking solution also includes a control system that can flexibly control the frequency, gating width, etc., of the driving signal, which makes it more convenient for subsequent laser amplification and makes it suitable for a variety of mode-locked lasers.