论文标题
基于Yolov3模型的海事场景中的实时目标检测
Real-Time target detection in maritime scenarios based on YOLOv3 model
论文作者
论文摘要
在这项工作中，提出了一个新颖的船只数据集，该数据集由超过56k的海洋船只图像组成，这些海洋船的图像通过网络搭配收集，包括12个船类别。基于KERAS API的YOLOV3单级检测器是在此数据集顶部构建的。目前在四个类别（货船，海军船，石油船和拖船）上的结果表明，平均精度为96％的联合（IOU）的交叉路口为0.5，令人满意的检测性能高达0.8。还实施了基于QT框架和DarkNet-53引擎的数据分析GUI服务，以简化部署过程，甚至对于没有数据科学专业知识的人，也可以分析大量图像。
In this work a novel ships dataset is proposed consisting of more than 56k images of marine vessels collected by means of web-scraping and including 12 ship categories. A YOLOv3 single-stage detector based on Keras API is built on top of this dataset. Current results on four categories (cargo ship, naval ship, oil ship and tug ship) show Average Precision up to 96% for Intersection over Union (IoU) of 0.5 and satisfactory detection performances up to IoU of 0.8. A Data Analytics GUI service based on QT framework and Darknet-53 engine is also implemented in order to simplify the deployment process and analyse massive amount of images even for people without Data Science expertise.
