This is the collection of Indian Traffic Sign Detection Dataset. This can be used maily on Traffic Sign detection projects using YOLO. Dataset is in YOLO format. There are 1264 total images in this dataset fully annotated using Labelimg tool. Some augmented datas using techniques like blurring, mosaic etc.. are also present. The dataset has images in 3 different types of traffic signs in India. Dataset is annotated only as one class-Traffic Sign.

This dataset contains a collection of videos consisting of satellite imagery augmented with 3D ship models, accompanied by the ships' corresponding AIS data. The intention of this dataset is for detecting dark ships, which are sea vessels acting maliciously, often while spoofing their AIS data. Multiple datasets exist that consist of satellite imagery of ships, however this dataset has the advantage of including each ships' corresponding AIS data. The simulated ships include both normal and anomalous behavior, whether the anomalous behavior is benign or malicious.

To ensure the usability and reliability of the collected data, one Hikvision monitoring camera (iDS-TCV900-AE/25) is deployed at the entrance of Taijia Expressway in Shanxi province in China for image capturing. This camera is installed on the roadside pole with a height of 5.8 meters and uses the infrared flash as the supplementary lighting. The captured images cover two lanes of the expressway, with the resolution being 4096*2160. All images are captured during the period of November 2019 to April 2020.

We present below a sample dataset collected using our framework for synthetic data collection that is efficient in terms of time taken to collect and annotate data, and which makes use of free and open source software tools and 3D assets. Our approach provides a large number of systematic variations in synthetic image generation parameters. The approach is highly effective, resulting in a deep learning model with a top-1 accuracy of 72% on the ObjectNet data, which is a new state-of-the-art result.