object detection

To enable intelligent vehicles and transportation systems, the vehicles and relevant systems need to have the ability to sense environment and recognize objects. In order to benefit from the robustness of radar for sensing, knowing how to use the radar system for effective object recognition is critical. Observing this, we in this paper propose a novel deep learning-aided object recognition system for radar systems by combining the You only look once (YOLO) system with a proposed object recheck system.

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In this dataset, we provided the raw analog-to-digital-converter (ADC) data of a 77GHz mmwave radar for the automotive object detection scenario. The overall dataset contains approximately 19800 frames of radar data as well as synchronized camera images and labels. For each radar frame, its raw data has 4 dimension: samples (fast time), chirps (slow time), transmitters, receivers. The experiment radar was assembled from the TI AWR 1843 board, with 2 horizontal transmit antennas and 4 receive antennas.

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455 Views

Federated learning (FL), as a privacy-preserving distributed machine learning algorithm is being rapidly applied in wireless communication networks, which enables IoT clients to obtain well-trained models while keeping data local.

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This dataset has a collection of 383 raw images of Indian vehicles in different illumination conditions using Infrared Day/Night Camera. The dataset resembles the Indian highway toll collection plazas. The dataset will be useful in developing intelligent models for applications such as automated toll collection, number plate detection and recognition, driverless vehicles, suspicious vehicle traction, and traffic management.

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582 Views

Fecal microscopic data set is a set of fecal microscopic images, which is used in object detection task. The datasets are collected from the Sixth People’s Hospital of Chengdu (Sichuan Province, China). The samples were went flow diluted, stirred and placed, and imaged with a microscopic imaging system. The clearest 5 images were collected for each view of each sample with Tenengrad definition algorithm. The dataset we collected includes 10670 groups of views with 53350 jpg images. The Resolution of images are 1200×1600. There are 4 categories, RBCs, WBCs, Molds, and Pyocytes.

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This dataset consists of the training and the evaluation datasets for the LiDAR-based maritime environment perception presented in our journal publication "Maritime Environment Perception based on Deep Learning." Within the datasets, LiDAR raw data are processed using Deep Neural Networks (DNN). In the training dataset, we introduce the method for generating training data in Gazebo simulation. In the evaluation datasets, we provide the real-world tests conducted by two research vessels, respectively.

 

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629 Views

MI3

Surveillance video captured by Multi-intensity infrared illuminator.

GT(ground-truths) :bounding boxes of 'person' in channel 2,4 and 6 by following the Pascal VOC format.

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480 Views

The accompanying dataset for the CVSports 2021 paper: DeepDarts: Modeling Keypoints as Objects for Automatic Scoring in Darts using a Single Camera

Paper Abstract:

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The dataset consists of 751 videos, each containing the performance one of the handball actions out of 7 categories (passing, shooting, jump-shot, dribbling, running, crossing, defence). The videos were manually extracted from longer videos recorded in handball practice sessions. 

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605 Views

SDU-Haier-AQD (Shandong University-Haier-Appearance Quality Detection) is an image dataset jointly constructed by Shandong University and Haier, which contains a various of air conditioner external unit image collected during actual detection process.The  Appearance Quality Detection (AQD) dataset is consisted of 10449 images, and the samples in the dataset are collected on the actual industrial production line of air conditioner.

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277 Views

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