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DIRS21-Dataset for Indian Road Scenarios
- Citation Author(s):
-
SauravKumar
Department of Electrical Engineering, Indian Institute of Technology Roorkee, India.SukumarNagineniDepartment of Electrical Engineering, Indian Institute of Technology Roorkee, India.SumathiParasuramanDepartment of Electrical Engineering, Indian Institute of Technology Roorkee, India. - Submitted by:
- Parasuraman Sumathi
- Last updated:
- Fri, 10/08/2021 - 03:53
- DOI:
- 10.21227/esy0-sm56
- Data Format:
- License:
3090 Views- Citations:
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Abstract
We propose a novel high-resolution dataset named, “Dataset for Indian Road Scenarios (DIRS21)” for developing perception systems for advanced driver assistance systems.
For data acquisition, we utilized 12- megapixel camera with an f/1.8 aperture with 1920 X 1080 pixels resolution. This dataset consists of 5093 images, those were collected under various scenes, weather, season, and illumination conditions. Each image is labelled with the name of the class to which it belongs. Our work considers the following seven classes of objects: Pedestrian, Rider, Car, Bus, Truck, NMV (Non-Motorized Vehicle), and EA (Electric Auto). Moreover, In our dataset, we consider (i) sedan and SUV in the same class called ‘Car’, (ii) minibus and Volvo bus are together grouped in the class ’Bus’, (iii) Cyclists and motorcyclists are placed in the class ’Rider’.
Please check the Readme file for further details.
Documentation
| Attachment | Size |
|---|---|
 Readme | 117.74 KB |
Comments
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Hi Saurav/Sukumar, I have a questions about your dataset and algorithm.
1.How good is your algorithm for reading terabytes of data? Will it perform optimally with large datasets?
2.What are the computation criteria for algorithms in datasets and training models?
3.Will it be compatible with current cloud technologies such as Azure, Google, and others?
4.How good is it in terms of I/O and throughput when dealing with large datasets?
Hi.. Sudheer.. Thank you .. actually, DIRS21 is only a dataset for the Indian road scenario. This dataset includes images from both urban and rural places, images from crowded and vacant roads, well-established highways as well as roads with many potholes, day as well as night conditions and so on. Also, The dataset is tested on various versions of YOLO (v3, v4, v5) and the result is quite satisfactory.