Image
Urdu Handwritten Ligature Dataset (UHLD) is the first unconstrained handwritten Urdu dataset developed for various handwritten Urdu recognition tasks and OCR research problems. The UHLD is written independently of paper color, paper type (blank or ruled), ink color, and pen type. The UHLD consists of around six thousand handwritten Urdu text lines written by 200 different writers. The UHLD dataset covers six and seven-character ligatures whereas it was only up to five character ligatures in previous dataset such as UNHD.
- Categories:
7 Views
The choice of the dataset is the key for OCR systems. Unfortunately, there are very few works on Telugu character datasets. The work by Pramod et al has 500 words and an average of 50 images with 50 fonts in four styles for training data each image of size 48x48 per category. They used the most frequently occurring words in Telugu but were unable to cover all the words in Telugu. Later works were based on character level. The dataset by Hastie has 460 classes and 160 samples per class which is made up of 500 images.
- Categories:
685 Views
FaceEngine is a face recognition database for using in CCTV based video surveillance systems. This dataset contains high-resolution face images of around 500 celebrities. It also contains images captured by the CCTV camera. Against each person folder, there are more than 10 images for that person. Face features can be extracted from this database. Also, there are test videos in the dataset that can be used to test the system. Each unique ID contains high resolution images that might help CCTV surveillance system test or training face detection model.
- Categories:
831 Views
This data set contains four types of road images: asphalt roads and gravel roads; Wading roads and snowy roads. It is mainly used to train road recognition models. Due to the large amount of original data, this data set only contains a part of road images. If you feel it is useful for your research, please email (wangzhangu1@163.com) to get the complete data set.
- Categories:
481 Views
ABSTRACT As the world increasingly becomes more interconnected, the demand for safety and security is ever-increasing, particularly for industrial networks. This has prompted numerous researchers to investigate different methodologies and techniques suitable for intrusion detection systems (IDS) requirements. Over the years, many studies have proposed various solutions in this regard including signature-based and machine-learning (ML) based systems. More recently, researchers are considering deep learning (DL) based anomaly detection approaches. Most proposed works in this research field aimed to achieve either one or a combination of high accuracy, considerably low false alarm rates (FARs), high classification specificity and detection sensitivity, achieving lightweight DL models, or other ML and DL-related performance measurement metrics. In this study, we propose a novel method to convert a raw dataset to an image dataset to magnify patterns. Based on this we devise an anomaly detection for IDS using a lightweight convolutional neural network (CNN) that classifies denial of service and distributed denial of service. The proposed methods were evaluated using a modern dataset, CSE-CIC-IDS2018, and a legacy dataset, NSL-KDD. We have also applied a combined dataset to assess the generalization of the proposed model across various datasets. Our experimental results have demonstrated that the proposed methods achieved high accuracy and considerably low FARs with high specificity and sensitivity. The resulting loss and accuracy curves have also demonstrated the excellent generalization of the proposed lightweight CNN model, effectively avoiding overfitting. This holds for both the modern and legacy datasets, including their mixed version.
- Categories:
658 Views
The deployment of unmanned aerial vehicles (UAV) for logistics and other civil purposes is consistently disrupting airspace security. Consequently, there is a scarcity of robust datasets for the development of real-time systems that can checkmate the incessant deployment of UAVs in carrying out criminal or terrorist activities. VisioDECT is a robust vision-based drone dataset for classifying, detecting, and countering unauthorized drone deployment using visual and electro-optical infra-red detection technologies.
- Categories:
7464 Views
The data set has been consolidated for the task of Human Posture Apprehension. The data set consists of two postures namely -
- Sitting and,
- Standing,
There are images for each of the postures listed above. The images have a dimension of 53X160 to 1845×4608.
- Categories:
1241 Views
WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.
- Categories:
1611 ViewsWITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.
- Categories:
40 Views
With the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.
- Categories:
324 Views