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**Data Description** The detection research of sonar images has a wide range of application value in many fields such as industry, environment and military. Sonar image data is often acquired together with water depth detection and bottom detection data, which enables us to observe the shallow structure of the seabed. This data set was launched by Pengcheng Laboratory, which is currently the largest and most extensive acoustic image data set in the industry. Please contact with yangj01@pcl.ac.cn if you have any questions. ** Download** *Baidu Cloud* link: https://pan.baidu.com/s/1X0Pcl1E-ctRkgJDpQowZEw password: 5lq2 *OneDrive* https://1drv.ms/u/s!AlpdPhejQ4FqiDL25YVhjxxT1WeU?e=DcwBRD **Training Set** The training set contains 4000 forward-looking sonar image files in .bmp format and corresponding annotation result files in .xml format. The file organization structure is as follows: ![](/attachments/download/968) The image data in the forward-looking sonar image file represents the acoustic reflection intensity information, and the principle is as follows: ![](/attachments/download/969) The pixel with the coordinate ![](/attachments/download/978) in the image represents the acoustic reflection intensity information at the direction ![](/attachments/download/976) and distance ![](/attachments/download/977) in the polar coordinate system. Among them, ![](/attachments/download/979) and ![](/attachments/download/980) respectively represent the horizontal angle and slant range of the forward looking sonar, ![](/attachments/download/981) and ![](/attachments/download/982) represent the horizontal and vertical size of the image respectively. The annotation result file contains the detection frame parameters (position and size) and target type of the target in the corresponding sonar image. Target types include cube, ball, cylinder, human body, tyre, circle cage, square cage, and Metal bucket and other 8 categories, the label format is as follows: ![](/attachments/download/970) The </sonar> field contains the working parameters of the sonar when the corresponding image is collected, such as the horizontal/vertical angle of the sonar, the range of the sonar slope, and the working frequency of the sonar. **Test Set** Test Set includes A_Board and B_Board, which contains 1000 sonar image in .bmp format and corresponding sonar annotation files in .xml respectively. The sonar parameter information file format is as follows: ![](/attachments/download/971) **Extra Information:** The data was collected using Tritech Gemini 1200i forward looking sonar. This data set is the original echo intensity information of the sonar in the form of a two-dimensional matrix, which is stored as a bmp image format for the convenience of process and can be read as 3-channel image data. The data of the 3 channels are equal, so the data of one channel can be used in actual processing; The original sonar image has not been adjusted for gain and is directly displayed as a nearly black image. It is suggested that the display effect of the data after histogram equalization is better, which helps to establish an intuitive understanding of the sonar image, but whether to add gain to the data should not have much influence on the algorithm design (the histogram equalization algorithm can be moved Discussion area for reference) **Data Example** ![](/attachments/download/972) The picture above is a forward-looking sonar image. The red box is the target marking frame. From left to right, they are square cage, ball, and tyre. ![](/attachments/download/973) The picture above is a forward-looking sonar image. The red box is the target marking frame. From left to right, they are human body, ball, and circle cage. ![](/attachments/download/974) The picture above is a forward-looking sonar image. The red box is the target marking frame. From left to right, they are tyre and metal bucket.

浏览量:836 下载量:0 项目类别: 图像
7 months前更新

针对列车卫星信号接收器的定位算法,建立一个用于追踪算法训练与改进的3D可视化孪生系统。

浏览量:932 下载量:5 项目类别: 精确定位
8 months前更新
OpenI Dolphin Python
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基于PyTorch的深度学习计算机视觉算法开源开放学习平台

浏览量:64 下载量:6 项目类别: 计算机视觉
9 months前更新

鹏城实验室开源水下声学数据集将分享每次实验实测的侧扫声呐数据集、前视声呐数据集等等,为水下声学领域提供数据支持。

浏览量:951 下载量:41 项目类别: 图像
11 months前更新

侧扫声呐图像标注软件(Open Side-Scan-Sonar Label Tool),简称**OpenSLT**,是一款能够对XTF格式文件直接进行标注的软件。其工作原理是在线解析XTF格式文件并生成图像,在此基础上进行标注,相比于传统标注方法,该软件能够有效的保留侧扫声呐图像的额外信息,如目标所在帧数、所在采样点、斜距信息等等,方便后续基于深度学习的目标检测算法开发。同时,该软件也支持以YOLO标注格式的输出,具备与传统标注软件相同的功能。

浏览量:1399 下载量:13 项目类别: 其他
11 months前更新

ubuntu下离线侧扫声呐数据xtf文件处理软件,输出基本信息、航迹、回波强度等数据文件,使用方法请查阅doc中的使用手册

浏览量:513 下载量:39 项目类别: 机器人
11 months前更新

本项目已经迁移到:https://git.openi.org.cn/OpenIOSSG/PLabel 开发。 PLabel即将发布新版本。(主要是升级mmdetection平台到V2.3.0,同时增加自动分割,集成人工标注的主动分类学习功能,标注工具栏中增加一些功能及一些Bug的解决。) 半自动标注系统是基于BS架构,由鹏城实验室自主研发,集成目标检测、视频跟踪、ReID分类、人脸检测等算法,实现了对图像,视频的自动标注,并可以人工对自动标注结果进行调整得到最终标注结果,同时也可以对视频、图片、医疗相关的数据进行人工标注。 半自动标注系统主要功能有:用户管理,数据集管理,自动标注,人工标注,ReID标注,车流统计,视频标注,医疗CT标注,超大图像标注,模型管理与重训,报表管理。数据标注过程一个非常重要的因素是数据安全,在标注使用中防止数据泄露,采用基于web标注工具是有效避免数据泄露的措施之一。 半自动标注系统以保证性能的情况下最小化人工标注代价为目标,不断提升自动标注效率,减少人工标注和人工参与过程。

浏览量:1931 下载量:196 项目类别: 图像
11 months前更新
eegtrack Python
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本工具包实现大批量、跨被试、跨设备的脑电数据和标签的预处理、采样、平衡、存储和机器学习数据集生成,涵盖分类、回归、自编码器、单样本模型调试等功能。需要Python 3.5以上的环境。

浏览量:426 下载量:2 项目类别: 深度学习
12 months前更新
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本项目提出了一种基于原型混合模型的小样本语义分割方法。该方法用于解决小样本语义分割中语义混叠的问题,在Pascal VOC 和COCO数据集上取得了显著的提升。 相关研究发表于ECCV 2020: "Prototype Mixture Models for Few-shot Semantic Segmentation" in European Conference on Computer Vision(ECCV 2020).

浏览量:461 下载量:5 项目类别: 语义分割
12 months前更新
pySNNGo Python
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本项目为SNNgo脉冲神经网络模型模拟器的前端开发工具,基于python开发,用于脉冲神经网络模型的描述和生成。

浏览量:350 下载量:10 项目类别: 其他
12 months前更新

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