Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Dataset of road surface images with seasons for machine learning applications.

ABSTRACT: Road surface monitoring plays a vital role in ensuring safety and comfort for the various road users, from pedestrians to drivers. Furthermore, this information is useful for the maintenance of the roads. The road condition deteriorates due to volatile weather. Thus the main objective of the proposed paper is to create an image dataset of the road surface for two seasons, i.e. summer and rainy. Accordingly, we created road surface images for different roads such as paved and unpaved roads. These folders consist of two subfolders for Rainy and Summer potholes. The dataset consists of 8484 images and 10 videos. This dataset is highly useful for machine learning experts working in the field of automatic vehicle controlling and road surface monitoring.

SUBMITTER: Bhutad S

PROVIDER: S-EPMC8933537 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Dataset of road surface images with seasons for machine learning applications.

Bhutad Sonali S Patil Kailas K

Data in brief 20220308

Road surface monitoring plays a vital role in ensuring safety and comfort for the various road users, from pedestrians to drivers. Furthermore, this information is useful for the maintenance of the roads. The road condition deteriorates due to volatile weather. Thus the main objective of the proposed paper is to create an image dataset of the road surface for two seasons, i.e. summer and rainy. Accordingly, we created road surface images for different roads such as paved and unpaved roads. These ...[more]

PMID: 35313491

Similar Datasets

VegNet: Dataset of vegetable quality images for machine learning applications.

Project description:The agricultural industry has an unmet requirement for quick and accurate classification or recognition of vegetables according to the quality criteria. This open research problem draws attention to the research scholars every time. The classification and object detection challenges have seen highly encouraging outcomes from machine learning and deep learning techniques. The foundational condition for developing precise and reliable machine learning models for the real-time context is a neat and clean dataset. With this goal in mind, we have developed a picture dataset of four popular vegetables in India that are also highly exported worldwide. In order to generate a dataset, we have taken into account four vegetables: Bell Peppers, Tomatoes, Chili Peppers, and New Mexico Chiles. The dataset is divided into four vegetable folders, including Bell Pepper, Tomato, Chili Pepper, and New Mexico Chile. Further each vegetable folder contains five subfolders namely (1) Unripe, (2) Ripe, (3) Old, and (4) Dried (5) Damaged. The image collection includes a total of 6850 pictures of vegetables in dataset. We firmly feel that the provided dataset is very beneficial for developing, evaluating, and validating a machine learning model for vegetable categorization or reorganization.

| S-EPMC9679474 | biostudies-literature

A road surface image dataset with detailed annotations for driving assistance applications.

Project description:The preview of the road surface states is essential for improving the safety and the ride comfort of autonomous vehicles. The created dataset in this data article consists of 370151 road surface images captured under a wide range of road and weather conditions in China. The original pictures are acquired with a vehicle-mounted camera and then the patches containing only the road surface area are cropped. The friction level, material, and unevenness properties of each road image are annotated in detail. This large-scale dataset is useful for developing vision-based road sensing modules to improve the performance of the driving assistance systems. Also, deep-learning experts can regard this dataset as a comparing benchmark for their algorithms. The dataset is available at [1].

| S-EPMC9343931 | biostudies-literature

Dry fruit image dataset for machine learning applications.

Project description:Dry fruits are convenient and nutritious snacks that can provide numerous health benefits. They are packed with vitamins, minerals, and fibres, which can help improve overall health, lower cholesterol levels, and reduce the risk of heart disease. Due to their health benefits, dry fruits are an essential part of a healthy diet. In addition to health advantage, dry fruits have high commercial worth. The value of the global dry fruit market is estimated to be USD 6.2 billion in 2021 and USD 7.7 billion by 2028. The appearance of dry fruits is utilized for assessing their quality to a great extent, requiring neat, appropriately tagged, and high-quality images. Hence, this dataset is a valuable resource for the classification and recognition of dry fruits. With over 11500+ high-quality processed images representing 12 distinct classes, this dataset is a comprehensive collection of different varieties of dry fruits. The four dry fruits included in this dataset are Almonds, Cashew Nuts, Raisins, and Dried Figs (Anjeer), along with three subtypes of each. This makes it a total of 12 distinct classes of dry fruits, each with its unique features, shape, and size. The dataset will be useful for building machine learning models that can classify and recognize different types of dry fruits under different conditions, and can also be beneficial for dry fruit research, education, and medicinal purposes. Due to their nutritional value and health advantages, dry fruits have been consumed for a very long time. One of the best strategies to improve general health is to include dry fruits in the diet.

| S-EPMC10333426 | biostudies-literature

Dataset of cannabis seeds for machine learning applications.

Project description:The recent changes in policies in several countries regarding cannabis use has increased cannabis usage and research [1,2]. Cannabis is the second most used psychoactive substance word-wide [3]. Cannabis remains the subject of many research works. The cannabis can be classified into different classes according to their external features like colour, shape, and size using some computer vision and machine learning techniques. Precise classification or recognition is the unmet need of the agriculture business. This attracts many researchers to produce solutions with machine learning and deep learning techniques. Neat and clean dataset is the primary requirements to build accurate and robust machine learning model and minimize misclassification for the real-time environment. To achieve this objective, we have created an image dataset of cannabis seed. Accordingly, we have considered seventeen cannabis seeds to create dataset. The dataset contains 17 subfolders of cannabis seeds and folder is named with the category of seed. We strongly believe the cannabis seeds dataset will be very helpful for training, testing, and validation of cannabis classification or recognition with machine learning models.

| S-EPMC9925849 | biostudies-literature

Sugarcane leaf dataset: A dataset for disease detection and classification for machine learning applications.

Project description:Sugarcane, a vital crop for the global sugar industry, is susceptible to various diseases that significantly impact its yield and quality. Accurate and timely disease detection is crucial for effective management and prevention strategies. We persent the "Sugarcane Leaf Dataset" consisting of 6748 high-resolution leaf images classified into nine disease categories, a healthy leaves category, and a dried leaves category. The dataset covers diseases such as smut, yellow leaf disease, pokkah boeng, mosale, grassy shoot, brown spot, brown rust, banded cholorsis, and sett rot. The dataset's potential for reuse is significant. The provided dataset serves as a valuable resource for researchers and practitioners interested in developing machine learning algorithms for disease detection and classification in sugarcane leaves. By leveraging this dataset, various machine learning techniques can be applied, including deep learning, feature extraction, and pattern recognition, to enhance the accuracy and efficiency of automated sugarcane disease identification systems. The open availability of this dataset encourages collaboration within the scientific community, expediting research on disease control strategies and improving sugarcane production. By leveraging the "Sugarcane Leaf Dataset," we can advance disease detection, monitoring, and management in sugarcane cultivation, leading to enhanced agricultural practices and higher crop yields.

| S-EPMC10964057 | biostudies-literature

Unmanned aerial vehicle (UAV) images of road vehicles dataset

Project description: Not available

| S-EPMC10950728 | biostudies-literature

FruitNet: Indian fruits image dataset with quality for machine learning applications.

Project description:Fast and precise fruit classification or recognition as per quality parameter is the unmet need of agriculture business. This is an open research problem, which always attracts researchers. Machine learning and deep learning techniques have shown very promising results for the classification and object detection problems. Neat and clean dataset is the elementary requirement to build accurate and robust machine learning models for the real-time environment. With this objective we have created an image dataset of Indian fruits with quality parameter which are highly consumed or exported. Accordingly, we have considered six fruits namely apple, banana, guava, lime, orange, and pomegranate to create a dataset. The dataset is divided into three folders (1) Good quality fruits (2) Bad quality fruits, and (3) Mixed quality fruits each consists of six fruits subfolders. Total 19,500+ images in the processed format are available in the dataset. We strongly believe that the proposed dataset is very helpful for training, testing and validation of fruit classification or reorganization machine leaning model.

| S-EPMC8668825 | biostudies-literature

Dataset of vehicle images for Indonesia toll road tariff classification.

Project description:Vehicle classifications with different methods have been applied for many purposes. The data provided in this article is useful for classifying vehicle purposes following the Indonesia toll road tariffs. Indonesia toll road tariff regulations divide vehicles into five groups as follows, group-1, group-2, group-3, group-4, and group-5, respectively. Group-1 is a class of non-truck vehicles, while group-2 to group-5 are classes of truck vehicles. The non-truck class consists of the sedan, pick-up, minibus, bus, MPV, and SUV. Truck classes are grouped based on the number of truck's axles. Group-2 is a class of trucks with two axles, a group-3 truck with three axles, a group-4 truck with four axles, and a group-5 truck with five axles or more. The dataset is categorized into five classes accordingly, which are group-1, group-2, group-3, group-4, and group-5 images. The data made available in this article observes images of vehicles obtained using a smartphone camera. The vehicle images dataset incorporated with deep learning, transfer learning, fine-tuning, and the Residual Neural Network (ResNet) model can yield exceptional results in the classification of vehicles by the number of axles.

| S-EPMC7394761 | biostudies-literature

Coffee and cashew nut dataset: A dataset for detection, classification, and yield estimation for machine learning applications.

Project description:Conventional methods of crop yield estimation are costly, inefficient, and prone to error resulting in poor yield estimates. This affects the ability of farmers to appropriately plan and manage their crop production pipelines and market processes. There is therefore a need to develop automated methods of crop yield estimation. However, the development of accurate machine-learning methods for crop yield estimation depends on the availability of appropriate datasets. There is a lack of such datasets, especially in sub-Saharan Africa. We present curated image datasets of coffee and cashew nuts acquired in Uganda during two crop harvest seasons. The datasets were collected over nine months, from September 2022 to May 2023. The data was collected using a high-resolution camera mounted on an Unmanned Aerial Vehicle . The datasets contain 3000 coffee and 3086 cashew nut images, constituting 6086 images. Annotated objects of interest in the coffee dataset consist of five classes namely: unripe, ripening, ripe, spoilt, and coffee_tree. Annotated objects of interest in the cashew nut dataset consist of six classes namely: tree, flower, premature, unripe, ripe, and spoilt. The datasets may be used for various machine-learning tasks including flowering intensity estimation, fruit maturity stage analysis, disease diagnosis, crop variety identification, and yield estimation.

| S-EPMC10788206 | biostudies-literature

BananaLSD: A banana leaf images dataset for classification of banana leaf diseases using machine learning.

Project description:Bananas, one of the most widely consumed fruits globally, are highly susceptible to various leaf spot diseases, leading to significant economic losses in banana production. In this article, we present the Banana Leaf Spot Diseases (BananaLSD) dataset, an extensive collection of images showcasing three prevalent diseases affecting banana leaves: Sigatoka, Cordana, and Pestalotiopsis. The dataset was used to develop the BananaSqueezeNet model [1]. The BananaLSD dataset contains 937 images of banana leaves collected from banana fields, which were then further augmented to generate another 1600 images. The images were acquired using three smartphone cameras in diverse real-world conditions. The dataset has potential for reuse in the development of machine learning models that can help farmers identify symptoms early. It can be useful for researchers working on leaf spot diseases and serve as motivation for further researches.

| S-EPMC10562173 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data