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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* https://pan.baidu.com/s/1J1m4jrLPeGdDc5sez75z9A password: 05dg *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.

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2 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。

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6 months前更新

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。

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7 months前更新
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OpenDS4All是一个旨在加速学术机构数据科学课程创建的项目。虽然数据科学有大量的在线材料,包括在线课程,但我们认识到,对许多学生(以及许多机构)来说,学习内容的最佳方式是通过讲座、背诵或翻转课堂活动和动手作业的结合。 OpenDS4All都试图填补这一重要的利基。我们的目标是为创建、定制和交付数据科学和数据工程教育提供建议、幻灯片集、Jupyter notebooks和其他材料。

原始仓库地址:https://github.com/odpi/opends4all.git

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9 months前更新

URPC 2020(大连)水下目标检测算法赛由国家自然科学基金委员会、大连市人民政府和鹏城实验室共同主办,大连市科技局、大连理工大学、大连金普新区管委会、大连金石滩国家旅游度假区管委会承办。本次比赛紧扣水下目标检测算法领域,创新的将人工智能与水下机器人进行有机结合,把真实水下环境的光学图像开放给更广泛的人工智能和算法研究群体,树立了一个目标检测和识别的新领域。

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9 months前更新

此存储库保存数据集和数据源服务的服务器组件

原始仓库地址:https://github.com/acumos/databroker-dataset.git

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9 months前更新
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本数据集为面向ReID研究方向的公开数据集,由云天励飞与鹏城实验室联合贡献,曾在全国人工智能大赛NAIC ReID赛项中作为入门级数据集进行使用。

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about 1 year前更新

水下目标检测算法赛是由国家自然基金委、鹏城实验室和湛江市人民政府联合主办全国水下机器人(湛江)大赛的第一阶段。本次比赛紧扣水下目标检测算法领域,创新的将人工智能与水下机器人进行有机结合,把真实水下环境的光学图像和声学图像开放给更广泛的人工智能和算法研究群体,树立了一个目标检测和识别的新领域。比赛分为「光学图像目标检测」和「声学图像目标检测」两个赛项。 本项目收集了「声学图像目标检测」赛项的相关数据集,希望这些数据能够对相关领域的研究者有所帮助。

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about 1 year前更新

水下目标检测算法赛是由国家自然基金委、鹏城实验室和湛江市人民政府联合主办全国水下机器人(湛江)大赛的第一阶段。本次比赛紧扣水下目标检测算法领域,创新的将人工智能与水下机器人进行有机结合,把真实水下环境的光学图像和声学图像开放给更广泛的人工智能和算法研究群体,树立了一个目标检测和识别的新领域。比赛分为「光学图像目标检测」和「声学图像目标检测」两个赛项。 本项目收集了「光学图像目标检测」赛项的相关数据集,希望这些数据能够对相关领域的研究者有所帮助。

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about 1 year前更新
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# Detecting Underwater Objects (DUO) 水下目标检测技术引起了人们的越来越多的关注。然而,这个领域仍然存在着若干挑战。我们通过应对以下挑战,来促进水下目标检测技术的发展。首先,由于目前可用的数据集基本上缺乏测试集的真值标注文件,这导致研究者必须在训练集上划分出测试集来与其他方法进行比较。训练其他方法会增加工作量;不同的研究人员会划分不同的测试集,导致这一领域没有一个统一的基准来比较不同算法的性能。其次,这些已有数据集(URPC系列)也存在其他缺点,如相似图像过多或标签标注不准确。针对以上这些挑战,我们在对所有相关数据集进行收集和重新标注的基础上,引入了一个新的数据集——水下目标检测数据集(Detection Underwater Objects, DUO)和其相应的基准(benchmark)。DUO包含了多种多样的水下图像,并且具有更合理的注释。相应的基准为学术研究和工业应用提供了多种目标检测模型(在mmddetection框架下)在DUO上的效率和准确性等指标对比数据,其中NVIDIA嵌入式平台JETSON AGX XAVIER也被用于评估不同检测模型的实时推理速度,用以模拟机器人的嵌入式环境。 ## 下载 * [BaiduYun](https://pan.baidu.com/s/1Be8zc9UdR_Pdsyotg_vR2Q) Key : 4bfl ## 引用 @ARTICLE{2021arXiv210605681L, author = {{Liu}, Chongwei and {Li}, Haojie and {Wang}, Shuchang and {Zhu}, Ming and {Wang}, Dong and {Fan}, Xin and {Wang}, Zhihui}, title = "{A Dataset And Benchmark Of Underwater Object Detection For Robot Picking}", journal = {arXiv e-prints}, year = 2021, month = jun, eid = {arXiv:2106.05681}, pages = {arXiv:2106.05681}, archivePrefix = {arXiv}, eprint = {2106.05681}, primaryClass = {cs.CV} }

原始仓库地址:https://github.com/chongweiliu/duo.git

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about 1 month前更新

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