Deep learning object detection methods, like YOLOv5, are effective in
identifying maritime vessels but often lack detailed information important for
practical applications. In this paper, we addressed this problem by developing
a technique that fuses Automatic Identification System (AIS) data with vessels
detected in images to create datasets. This fusion enriches ship images with
vessel-related data, such as type, size, speed, and direction. Our approach
associates detected ships to their corresponding AIS messages by estimating
distance and azimuth using a homography-based method suitable for both fixed
and periodically panning cameras. This technique is useful for creating
datasets for waterway traffic management, encounter detection, and
surveillance. We introduce a novel dataset comprising of images taken in
various weather conditions and their corresponding AIS messages. This dataset
offers a stable baseline for refining vessel detection algorithms and
trajectory prediction models. To assess our method's performance, we manually
annotated a portion of this dataset. The results are showing an overall
association accuracy of 74.76 %, with the association accuracy for fixed
cameras reaching 85.06 %. This demonstrates the potential of our approach in
creating datasets for vessel detection, pose estimation and auto-labelling
pipelines.

通过融合自动识别系统（AIS）数据和图像中检测到的船舶，我们提出了一种方法用于创建数据集，用于识别船只、姿态估计和自动标注管道。

海洋计算机视觉应用的图像与 AIS 数据融合技术

Image and AIS Data Fusion Technique for Maritime Computer Vision  Applications

Segmentation of tubular structures in vascular imaging is a well studied
task, although it is rare that we try to infuse knowledge of the tree-like
structure of the regions to be detected. Our work focuses on detecting the
important landmarks in the vascular network (via CNN performing both
localization and classification of the points of interest) and representing
vessels as the edges in some minimal distance tree graph. We leverage geodesic
methods relevant to the detection of vessels and their geometry, making use of
the space of positions and orientations so that 2D vessels can be accurately
represented as trees. We build our model to carry tracking on Ultrasound
Localization Microscopy (ULM) data, proposing to build a good cost function for
tracking on this type of data. We also test our framework on synthetic and eye
fundus data. Results show that scarcity of well annotated ULM data is an
obstacle to localization of vascular landmarks but the Orientation Score built
from ULM data yields good geodesics for tracking blood vessels.

通过使用卷积神经网络对感兴趣的点进行定位和分类，并将血管表示为一些最小距离树状图的边，基于地形测量方法检测血管及其几何特征，我们的研究聚焦于在血管网络中检测重要的地标，并利用位置和方向的空间，准确地将 2D 血管表示为树状结构。我们构建了适用于超声定位显微镜（ULM）数据跟踪的模型，提出在这种类型数据上构建良好的成本函数用于跟踪。我们还对合成数据和眼底图像数据进行了测试。结果表明，缺乏良好注释的 ULM 数据是血管地标定位的障碍，但基于 ULM 数据建立的方向得分对于追踪血管具有良好的测地线效果。

利用 CNN 和测地线拟合树模型以跟踪血管并应用于超声定位显微镜数据

Fitting tree model with CNN and geodesics to track vesselsand  application to Ultrasound Localization Microscopy data

Culling newly hatched male chicks in industrial hatcheries poses a serious
ethical problem. Both laying and broiler breeders need males, but it is a
problem because they are produced more than needed. Being able to determine the
sex of chicks in the egg at the beginning or early stage of incubation can
eliminate ethical problems as well as many additional costs. When we look at
the literature, the methods used are very costly, low in applicability,
invasive, inadequate in accuracy, or too late to eliminate ethical problems.
Considering the embryo's development, the earliest observed candidate feature
for sex determination is blood vessels. Detection from blood vessels can
eliminate ethical issues, and these vessels can be seen when light is shined
into the egg until the first seven days. In this study, sex determination was
made by morphological analysis from embryonic vascular images obtained in the
first week when the light was shined into the egg using a standard camera
without any invasive procedure to the egg.

在研究中通过无侵入的方法，对鸡胚在孵化的第一周通过血管成像来进行形态分析以实现早期性别鉴定，从而消除道德问题和降低成本。