Target Detection on Water Surfaces Using Fusion of Camera and LiDAR Based Information

To address the challenges of missed detections in water surface target detection using solely visual algorithms in unmanned surface vehicle(USV)perception,this paper proposes a method based on the fusion of visual and LiDAR point-cloud projection for water surface target detection.Firstly,the visual recognition component employs an improved YOLOv7 algorithmbased on a self-built dataset for the detection of water surface targets.This algorithm modifies the original YOLOv7 architecture to a Slim-Neck structure,addressing the problemof excessive redundant information during feature extraction in the original YOLOv7 network model.Simultaneously,this modification simplifies the computational burden of the detector,reduces inference time,and maintains accuracy.Secondly,to tackle the issue of sample imbalance in the self-built dataset,slide loss function is introduced.Finally,this paper replaces the original Complete Intersection over Union(CIoU)loss function with the Minimum Point Distance Intersection over Union(MPDIoU)loss function in the YOLOv7 algorithm,which accelerates model learning and enhances robustness.To mitigate the problem of missed recognitions caused by complex water surface conditions in purely visual algorithms,this paper further adopts the fusion of LiDAR and camera data,projecting the threedimensional point-cloud data from LiDAR onto a two-dimensional pixel plane.This significantly reduces the rate of missed detections for water surface targets.

A Small Target Detection Method for Sea Surface Based on Guided Filtering and Local Mean Gray Difference

The traditional small target detection algorithm often results in a high false alarm rate on the sea surface background. To address this issue, a small target detection method based on guided filtering and local average gray level difference was proposed in this paper for the sea surface. Firstly, the method enhanced the details of the small targets by employing guided filtering to suppress the background clutter and noise in the sea surface image. Subsequently, the local average gray level difference of each point in the image was calculated to further distinguish the targets from other interference points. Finally, the threshold segmentation method was utilized to obtain the actual small targets on the sea surface. After conducting experiments on various sea surface scenes, the LSCRG, BSF, and ROC curve were computed for the proposed method and five other algorithms. Comparative analysis with BS, Top-hat, TDLMS, Max-median, and LCM demonstrates the superiority of the proposed method for infrared small target detection on the sea surface.

Variable structure guidance law for attacking surface maneuver targets

The characteristics of surface maneuver targets are analyzed and a 3-D relative motion model for missiles and targets is established. A variable structure guidance law is designed considering the characteristics of targets. In the guidance law, the distance between missiles and targets as well as the missile-target relative velocity are all substituted by estimation values. The estimation errors, the target＇s velocity, and the maneuver acceleration are all treated as bounded disturbance. The guidance law proposed can be implemented conveniently in engineering with little target information. The performance of the guidance system is analyzed theoretically and the numerical simulation result shows the effectiveness of the guidance law.

Multiple extended target tracking algorithm based on Gaussian surface matrix

In this paper, we consider the problem of irregular shapes tracking for multiple extended targets by introducing the Gaussian surface matrix（GSM） into the framework of the random finite set（RFS） theory. The Gaussian surface function is constructed first by the measurements, and it is used to define the GSM via a mapping function. We then integrate the GSM with the probability hypothesis density（PHD） filter, the Bayesian recursion formulas of GSM-PHD are derived and the Gaussian mixture implementation is employed to obtain the closed-form solutions. Moreover, the estimated shapes are designed to guide the measurement set sub-partition, which can cope with the problem of the spatially close target tracking. Simulation results show that the proposed algorithm can effectively estimate irregular target shapes and exhibit good robustness in cross extended target tracking.

Threat Assessment Method Considering Target Instantaneous and Historical States

The sea surface escort formation faces various threats in reality. For example, suicide boats may carry explosives or other dangerous items, aiming to cause maximum damage by colliding or detonating escort targets. Since suicide boats have a certain degree of concealment, it is necessary to establish a threat assessment algorithm to timely identify and respond to such fast and concealed threats. This paper establishes a threat assessment model that considers the instantaneous and historical states of the target. The instantaneous state of the target takes into account six evaluation indicators, including target category, target distance, target heading, target speed, collision risk, and ship automatic identification system(AIS) recognition status;in terms of historical state information mining, a target typical intention recognition method based on graph neural network is proposed to achieve end-to-end target typical intention recognition. Furthermore, this paper introduces a multi-attribute decision analysis method to weight the evaluation indicators, improves the relative closeness calculation method between different evaluation schemes and positive and negative ideal schemes, and determines the target threat ranking based on relative closeness. Based on Unity3D, a set of unmanned boat confrontation simulation system is designed and developed, and typical intention recognition data sets and threat assessment scenario simulation data are generated through real-life confrontation. Comparative analysis shows that the threat assessment model in this paper can accurately and timely detect raid target threats and give scientific and reasonable target threat ranking results.

Sulfur speciation defined subcellular localization of coumarin derivatives:Correlation of structural relationship to biological behaviors

Targeted-delivery is of great importance to molecular probes and drugs for cell biology study. Herein we reported 11 sulfur-containing coumarins as cell imaging probes. Different sulfur speciation of the 4 representative coumarins SC1-SC4 renders them significantly different subcellular localizations and cellular uptake pathways： SC1 containing thioether group located in lysosomes, while sulfoxide and sulfone compounds SC2 and SC3 distributed in the whole cell. Furthermore, the cationic sulfonium containing compound SC4 was internalized by clathrin-mediated endocytosis and localized at mitochondria. By analyzing the molecular parameters of all 11 coumarins, we found that different sulfur speciation affected their lipophilicity and electrostatic surface potential. These two key factors play roles in altering biological behaviors of the coumarins. The results revealed the importance of sulfur speciation on the physicochemical properties and thus subcellular localization of bioprobes. This is useful for designing new functional bioprobes.