Terrain surveillance system with drone and applied machine vision

Abstract Road accidents are a major contribution to the Annual death rates all over the world. India, ranks first globally in the number of fatalities from road accidents. According to the Ministry of Roads & Transportation, India saw over 440,000 road accidents in 2019. As a result, over 150,000 lives were lost. Poor road conditions contribute to these directly and indirectly. In India, safety standards and conditions of roads are maintained by local bodies in a given area of jurisdiction. While there have been several attempts at improving the quality of roads, weren’t instrumental in giving proper results [42]. A recent study suggested that Artificial Intelligence (AI) might help achieve the goals. Some of the AI applications have had better results when powered with Computer Vision. While computer vision has been previously used to identify faults in roads, it is not widely implemented or made available for public use. Road inspection still largely remains a time-consuming manual task, hindering the maintenance process in most cities. Moreover, being unaware of unattended faults on roads is often the cause of road accidents, especially in rough weather conditions that make it impossible for drivers to visually gauge any dangers on their route. The proposed model uses a transfer-learning approach; using Mask R-CNN in identifying the defects at an instance level segmentation. As adding this, it requires less labelling and an additional mask helps in blocking out extra noise around the images. This paper trains a Mask R-CNN architecture-based model to identify potholes, discontinuous roads, blind spots, speed bumps, and the type of road--gravel, concrete, asphalt, tar, or mud--with a dataset of images obtained from a drone. The model is further trained to create depth maps and friction estimates of the roads being surveyed. Once trained, the model is tested on a drone-captured live feed of roads in Chennai, India. The results, once sufficiently accurate, will be implemented in a practical application to help users assess road conditions on their path.

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Integrating IoT and Blockchain for Ensuring Road Safety: An Unconventional Approach

Sensors ◽

10.3390/s20113296 ◽

2020 ◽

Vol 20 (11) ◽

pp. 3296 ◽

Cited By ~ 2

Author(s):

Deepak Prashar ◽

Nishant Jha ◽

Sudan Jha ◽

Gyanendra Prasad Joshi ◽

Changho Seo

Keyword(s):

Communication Networks ◽

Intelligent Transportation System ◽

Road Traffic ◽

The Internet ◽

Road Accidents ◽

Blockchain Technology ◽

Proposed Model ◽

Proper Design ◽

The Internet Of Things

The Internet of things (IoT), the Internet of vehicles, and blockchain technology have become very popular these days because of their versatility. Road traffic, which is increasing day by day, is causing more and more deaths worldwide. The world needs a product that would reduce the number of road accidents. This paper suggests combining IoT and blockchain technology to mitigate road hazards. The new intelligent transportation system technologies and the subsequent emergence of 5G technologies will be a blessing, delivering the necessary speed to ensure both safety and quality of service (QoS). Hashgraph technology, a distributed ledger technology is used to create communication networks between the different vehicles and other relevant parameters. Scheduling the requests according to the priorities for ensuring better QoS quotient can be effectively done using hashgraph. We demonstrated how the hashgraph outstrips other equivalents platforms. The proposed model was simulated using OMNeT++ with proper design and network description files. A hardware implementation of the proposed model was also done. Messages were transferred between the vehicles and prioritized using a hashgraph. This paper proposes an effective model in reducing the accidents in terms of parameters like speed, security, stability, and fairness.

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Vehicle Fleet Management System and Safety Functions

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f8463.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 2706-2714

Keyword(s):

Real Time ◽

Road Accidents ◽

Oil Consumption ◽

Time Data ◽

Night Time ◽

Negative Results ◽

Proposed Model ◽

Sensor Module ◽

Vehicle Fleet

All over the world use of vehicles is increasing day by day. As well as accident ratio is also increasing with increase in number of vehicles. Maintaining regular service schedule for customers to become difficult in their day to day busy life. Proposed model detects oil consumption and coolant consumption concerns to real-time weather statics with the help of sensors. This system is designed by considering car safety. Which helps to reduce everyday rate of accident occur in all countries. As well as for automobile industries to improve the quality of production in future cars. As per the survey of India, 496762 number of accidents occur in 2016. The use of bright headlight at night time became one of the causes of road accidents. To overcome this problem designed model uses a microcontroller to operate all systems. Subsystems like sensors, RFID module, GPS module, LDR sensor module, Wi-Fi module are connected to the microcontroller to take real-time readings. So that, Microcontroller distinguish all positive and negative results. As per that remaining subsystem take respective action to avoid accidents. Also, the system provides gathering of real-time data with the help of sensors from cars to improve the quality of the product.

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Water quality of Tampa Bay, Florida : wet-weather conditions, October 1971

Open-File Report ◽

10.3133/otherfl75005 ◽

1975 ◽

Author(s):

Carl R. Goodwin ◽

Joseph S. Rosenshein ◽

D.M. Michaelis

Keyword(s):

Water Quality ◽

Weather Conditions ◽

Tampa Bay ◽

Wet Weather

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Model and Method for Contributor’s Quality Assessment in Community Image Tagging Systems

Information and Control Systems ◽

10.31799/1684-8853-2018-4-45-51 ◽

2018 ◽

pp. 45-51

Author(s):

A. V. Ponomarev

Keyword(s):

Large Scale ◽

Wide Spectrum ◽

Preference Relation ◽

Pairwise Comparison ◽

Ground Truth ◽

Comparison Method ◽

Characteristic Matrix ◽

Image Tagging ◽

Proposed Model

Introduction: Large-scale human-computer systems involving people of various skills and motivation into the information processing process are currently used in a wide spectrum of applications. An acute problem in such systems is assessing the expected quality of each contributor; for example, in order to penalize incompetent or inaccurate ones and to promote diligent ones.Purpose: To develop a method of assessing the expected contributor’s quality in community tagging systems. This method should only use generally unreliable and incomplete information provided by contributors (with ground truth tags unknown).Results:A mathematical model is proposed for community image tagging (including the model of a contributor), along with a method of assessing the expected contributor’s quality. The method is based on comparing tag sets provided by different contributors for the same images, being a modification of pairwise comparison method with preference relation replaced by a special domination characteristic. Expected contributors’ quality is evaluated as a positive eigenvector of a pairwise domination characteristic matrix. Community tagging simulation has confirmed that the proposed method allows you to adequately estimate the expected quality of community tagging system contributors (provided that the contributors' behavior fits the proposed model).Practical relevance: The obtained results can be used in the development of systems based on coordinated efforts of community (primarily, community tagging systems).

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Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

Applied Sciences ◽

10.3390/app11062838 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2838

Author(s):

Nikitha Johnsirani Venkatesan ◽

Dong Ryeol Shin ◽

Choon Sung Nam

Keyword(s):

Neural Network ◽

Radiation Dose ◽

Convolutional Neural Network ◽

Model Performance ◽

Performance Comparison ◽

Apache Spark ◽

Training Time ◽

Learning Framework ◽

Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

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Clay-Based Brick Porosity Estimation Using Image Processing Techniques

Journal of Intelligent Systems ◽

10.1515/jisys-2018-0191 ◽

2019 ◽

Vol 29 (1) ◽

pp. 1226-1234

Author(s):

Safa Jida ◽

Hassan Ouallal ◽

Brahim Aksasse ◽

Mohammed Ouanan ◽

Mohamed El Amraoui ◽

...

Keyword(s):

Electron Microscopy ◽

Image Processing ◽

Scanning Electron Microscopy ◽

Computer Vision ◽

Image Processing Techniques ◽

Processing Techniques ◽

Porosity Estimation ◽

Microscopy Images ◽

Scanning Electron

Abstract This work intends to apprehend and emphasize the contribution of image-processing techniques and computer vision in the treatment of clay-based material known in Meknes region. One of the various characteristics used to describe clay in a qualitative manner is porosity, as it is considered one of the properties that with “kill or cure” effectiveness. For this purpose, we use scanning electron microscopy images, as they are considered the most powerful tool for characterising the quality of the microscopic pore structure of porous materials. We present various existing methods of segmentation, as we are interested only in pore regions. The results show good matching between physical estimation and Voronoi diagram-based porosity estimation.

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Prediction of Pest Insect Appearance Using Sensors and Machine Learning

Sensors ◽

10.3390/s21144846 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4846

Author(s):

Dušan Marković ◽

Dejan Vujičić ◽

Snežana Tanasković ◽

Borislav Đorđević ◽

Siniša Ranđić ◽

...

Keyword(s):

Machine Learning ◽

Relative Humidity ◽

Weather Conditions ◽

Daily Basis ◽

Machine Learning Algorithms ◽

Lower Percentage ◽

Timely Manner ◽

Proposed Model ◽

Set Up ◽

Accuracy Of Prediction

The appearance of pest insects can lead to a loss in yield if farmers do not respond in a timely manner to suppress their spread. Occurrences and numbers of insects can be monitored through insect traps, which include their permanent touring and checking of their condition. Another more efficient way is to set up sensor devices with a camera at the traps that will photograph the traps and forward the images to the Internet, where the pest insect’s appearance will be predicted by image analysis. Weather conditions, temperature and relative humidity are the parameters that affect the appearance of some pests, such as Helicoverpa armigera. This paper presents a model of machine learning that can predict the appearance of insects during a season on a daily basis, taking into account the air temperature and relative humidity. Several machine learning algorithms for classification were applied and their accuracy for the prediction of insect occurrence was presented (up to 76.5%). Since the data used for testing were given in chronological order according to the days when the measurement was performed, the existing model was expanded to take into account the periods of three and five days. The extended method showed better accuracy of prediction and a lower percentage of false detections. In the case of a period of five days, the accuracy of the affected detections was 86.3%, while the percentage of false detections was 11%. The proposed model of machine learning can help farmers to detect the occurrence of pests and save the time and resources needed to check the fields.

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Robust Environmental Sensing Using UAVs

ACM Transactions on Internet of Things ◽

10.1145/3464943 ◽

2021 ◽

Vol 2 (4) ◽

pp. 1-20

Author(s):

Ahmed Boubrima ◽

Edward W. Knightly

Keyword(s):

Air Pollution ◽

Gas Sensors ◽

High Performance ◽

Weather Conditions ◽

Ambient Air ◽

Mission Planning ◽

Pollution Monitoring ◽

Compound Pollution ◽

Planning Algorithm

In this article, we first investigate the quality of aerial air pollution measurements and characterize the main error sources of drone-mounted gas sensors. To that end, we build ASTRO+, an aerial-ground pollution monitoring platform, and use it to collect a comprehensive dataset of both aerial and reference air pollution measurements. We show that the dynamic airflow caused by drones affects temperature and humidity levels of the ambient air, which then affect the measurement quality of gas sensors. Then, in the second part of this article, we leverage the effects of weather conditions on pollution measurements’ quality in order to design an unmanned aerial vehicle mission planning algorithm that adapts the trajectory of the drones while taking into account the quality of aerial measurements. We evaluate our mission planning approach based on a Volatile Organic Compound pollution dataset and show a high-performance improvement that is maintained even when pollution dynamics are high.

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A Hindi Image Caption Generation Framework Using Deep Learning

ACM Transactions on Asian and Low-Resource Language Information Processing ◽

10.1145/3432246 ◽

2021 ◽

Vol 20 (2) ◽

pp. 1-19

Author(s):

Santosh Kumar Mishra ◽

Rijul Dhir ◽

Sriparna Saha ◽

Pushpak Bhattacharyya

Keyword(s):

Computer Vision ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

English Language ◽

Image Captioning ◽

Textual Description ◽

Proposed Model ◽

Hindi Language ◽

The Given

Image captioning is the process of generating a textual description of an image that aims to describe the salient parts of the given image. It is an important problem, as it involves computer vision and natural language processing, where computer vision is used for understanding images, and natural language processing is used for language modeling. A lot of works have been done for image captioning for the English language. In this article, we have developed a model for image captioning in the Hindi language. Hindi is the official language of India, and it is the fourth most spoken language in the world, spoken in India and South Asia. To the best of our knowledge, this is the first attempt to generate image captions in the Hindi language. A dataset is manually created by translating well known MSCOCO dataset from English to Hindi. Finally, different types of attention-based architectures are developed for image captioning in the Hindi language. These attention mechanisms are new for the Hindi language, as those have never been used for the Hindi language. The obtained results of the proposed model are compared with several baselines in terms of BLEU scores, and the results show that our model performs better than others. Manual evaluation of the obtained captions in terms of adequacy and fluency also reveals the effectiveness of our proposed approach. Availability of resources : The codes of the article are available at https://github.com/santosh1821cs03/Image_Captioning_Hindi_Language ; The dataset will be made available: http://www.iitp.ac.in/∼ai-nlp-ml/resources.html .

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Economic Analysis of a Transport Company in the Aspect of Car Vehicle Operation

Sustainability ◽

10.3390/su13010427 ◽

2021 ◽

Vol 13 (1) ◽

pp. 427

Author(s):

Magdalena Rykała ◽

Łukasz Rykała

Keyword(s):

Weather Conditions ◽

Neural Model ◽

Motor Vehicles ◽

Bulk Materials ◽

The Sustainable Development ◽

Average Temperature ◽

Transport Services ◽

Vehicle Operation ◽

The Impact

The article describes the issues of transport of bulk materials. The knowledge of this process has a key impact on the rational planning of transport tasks. It is necessary to have knowledge about the transport services market and the competition that exists in it. In order to achieve a competitive advantage on the market, enterprises should analyze data on the implementation of transport tasks on an ongoing basis. It is also important that the costs incurred from the conducted activity are minimized, while increasing the quality of services and taking into account the sustainable development of the enterprise. The study analyzes data from a few selected motor vehicles in the period of 3 years of operation, coming from an enterprise specializing in the transport of bulk materials. Moreover, a global sensitivity analysis was performed based on a neural model describing the impact of the analyzed factors on the company’s profit. The results show that the most important factors influencing the company’s profit are the fuel consumption of individual vehicles, the driver (driving style) and the month (average temperature, weather conditions).

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