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Drone-Based applications for Tailings Dam Monitoring

Drone-Based Applications for Tailings Dam Monitoring

引用

APCOM 2023 conference: Intelligent Mining: Innovation, vision, and Value

作者： Gomez, Jose A. Sattarvand, Javad Department of Mining & Metallurgical Engineering Mackay School of Earth Sciences & Engineering University of Nevada RenoNV United States

ISBN: (纸本)9780873355216

Failures of tailings dams have been happening lately. Due to the lack of laws on particular design criteria and stability requirements related monitoring during construction and maintenance, they are thought to be more fragile than hydraulic dams. Monitoring the dam is therefore necessary to understand its current condition and guarantee its safety. The physical condition of the dam could be evaluated with the early identification of seepage. Additionally, due to their adaptability and capacity for high-resolution data collecting, UAVs are an excellent choice for efficiently covering the tailings dam site. UAVs may capture high-quality photos when equipped with a high-resolution RGB camera, thermal sensors, or multispectral sensors. When these sensors are paired with image processing and machine learning algorithms, the result is a reliable estimate of the dam condition. © 2023 Society for Mining, Metallurgy & Exploration Inc. All rights reserved.

关键词： Learning algorithms

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Feature Extraction from image Sensing (Remote) : image Segmentation 5

Feature Extraction from Image Sensing (Remote) : Image Segme...

引用

5th IEEE International conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Chauhan, Kaushal Tomar, Harshit Kamal, Kushagra Goel, Pallavi Galgotias College Of Engineering And Technology Department Of Computer Science And Engineering Greater Noida India

ISBN: (纸本)9798350330861

In this research paper, an overview of computer methods for segmenting continuous-tone images into meaningful parts and characterizing these parts with 'features' is presented. image segmentation is an essential pre-processing step in computer vision and image processing softwares. This paper discusses various techniques and algorithms used for image segmentation, including both supervised and unsupervised methods. The performance of these techniques is evaluated on different test images, and future research directions are proposed. Feature extraction from image sensing is a critical step in multimedia processing. This paper explores the process of feature extraction from remotely sensed images. With the increasing use of remote sensing technology, it is crucial to have effective and accurate methods for extracting features from remotely sensed data, which are utilized in various applications such as land-use mapping, environmental proctoring, and in natural-disaster management. This paper discusses several techniques for feature extraction, including pixel-based, object-based, and machine learning-based approaches. Additionally, it highlights the difficulties associated with feature extraction from remote sensing data, such as atmospheric effects, radiometric and geometric distortions, and the presence of noise. The paper concludes by discussing future directions in remote sensing feature extraction, including the integration of deep learning methods and the development of more robust methods has the ability of handling large-scale datasets. With the help of deep learning methods and algorithms, especially CNN's, has tremendously improved the accuracy of image scene classification. In this study, a CNN model which is already trained was utilized to fetch-out deep features from images at various network layers, including the average pooling layer. © 2023 IEEE.

关键词： image segmentation

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Multichannel Object Detection with Event Camera

Multichannel Object Detection with Event Camera

引用

International image processing, applications and Systems conference (IPAS)

作者： Rafael Iliasov Alessandro Golkar Chair of Spacecraft Systems Technical University of Munich Munich Germany

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

object detection based on event vision has been a dynamically growing field in computer vision for the last 16 years. In this work, we create multiple channels from a single event camera and propose an event fusion method (EFM) to enhance object detection in event-based vision systems. Each channel uses a different accumulation buffer to collect events from the event camera. We implement YOLOv7 for object detection, followed by a fusion algorithm. Our multichannel approach outperforms single-channel-based object detection by 0.7% in mean Average Precision (mAP) for detection overlapping ground truth with IOU = 0.5.

关键词： Computer vision Event detection machine vision AI accelerators Object detection Cameras Feature extraction Real-time systems

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Application of the image processing Technique for Powerline Robot 11th

Application of the Image Processing Technique for Powerline ...

引用

11th EAI International conference on Context-Aware Systems and applications, ICCASA 2022

作者： Ngo, Ha Quang Thinh Bui, The Tri 268 Ly Thuong Kiet Street District 10 Ho Chi Minh City Viet Nam Linh Trung Ward Thu Duc City Ho Chi Minh City Viet Nam

ISBN: (纸本)9783031288159

Applying image processing to electromechanical systems is a problem of interest to scientists, in order to serve humans in many fields. To do that, there needs to be a connection between image processing and mechanical construction to create complete mobile cameras. One of the research directions is about mobile cameras, specifically a system consisting of dual cameras that detect and track moving objects, and at the same time calculate the distance from the dual camera system to the target, this system can be application in object tracking robot. In this paper, the research object includes the camera system designed according to the pan-tilt structure, the algorithm used for object detection is YOLO-based on CNN, estimating the distance from the camera system to the object. By means of stereo vision, control the pan-tilt system to automatically track objects. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： machine learning

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Detection and Counting of Nuts and Bolts with image processing Using MATLAB 1

引用

2nd conference of Innovative Product Design and Intelligent Manufacturing System, IPDIMS 2020

作者： Murali, Gunji Bala Vasant, Gaikwad Tejas Kupwade, Dhananjay D. Girish, Deshmukh Atharva Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (数字)9789811902963

ISBN: (纸本)9789811902956

machine vision system plays vital roles in the industrial application in order to maintain quality and control the process. machine vision technology has numerous applications in various industries like automotive industry, pharmaceutical industries, food and beverage, electronics, packages, and process control. The purpose behind this work is the awareness of machine vision technology and the identification of mechanical components particularly nuts and bolts. An automotive uses nuts and bolts and are assembled at different stages of assembly. The count of these nuts and bolts becomes vital in order to avoid shortage at any stage of assembly. Thus, sorting of the nuts and bolts and their counting is necessary in order to save time. The system can be designed using different algorithms, and all algorithms can be integrated using MATLAB software. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image processing technique machine vision MATLAB Nuts bolts counting

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Detection of Paddy Plant Diseases Using Google Teachable machine 6th

Detection of Paddy Plant Diseases Using Google Teachable Mac...

引用

6th International conference on Soft Computing and Data Mining (SCDM)

作者： Ramli, Nor Azuana Pratondo, Agus Sulaiman, Sahimel Azwal Yusoff, Wan Nur Syahidah Wan Abu, Noratikah Univ Malaysia Pahang Al Sultan Abdullah Ctr Math Sci Kuantan 26300 Pahang Malaysia Telkom Univ Sch Appl Sci Selaru lt 3Jl Telekomunikasi 1 Bandung 40257 West Java Indonesia

ISBN: (纸本)9783031669644;9783031669651

Malaysia faced a shortage of rice at the end of the previous year. Due to this shortage, the government had to import rice to meet the needs of the people, at the same time affecting the national economy. The shortage of rice can be caused by many factors and one of them is because of the decrease in rice production productivity due to the failure to prevent diseases that affect crop yields earlier. If no control measures are implemented after the infection begins, the disease may cause rice yield losses of up to half of the production. Hence, early detection of these diseases is important for effective management and control strategies. This study aims to develop a model that is able to detect paddy plant disease by using Google Teachable machine. This model is compared to the model developed using You Only Look Once (YOLO) version 8. As the primary dataset has not been collected yet, this study utilized dataset from the Internet. Overall, our findings highlight the model developed using Google Teachable machine outperforms the model developed using YOLOv8 in terms of accuracy, simplicity and performance time as it can be completed in half an hour compared toYOLOv8 which took 2.083 h to complete the training. For future study, the model from Teachable machine will be deployed through mobile applications for disease monitoring using data from drones.

关键词： paddy plant disease computer vision image processing

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Computer-vision-Based Industrial Algorithm for Detecting Fruit and Vegetable Dimensions and Positioning 19th

Computer-Vision-Based Industrial Algorithm for Detecting Fr...

引用

19th International conference on Soft Computing Models in Industrial and Environmental applications, SOCO 2024

作者： Guevara, Cesar Rostan, Julen Rodriguez, Javier Gonzalez, Silvia Sedano, Javier C. López Bravo 70 Burgos09001 Spain

ISBN: (纸本)9783031750090

A computer-vision-based industrial algorithm is proposed in this study for the detection of the dimensions and the spatial positioning of fruit and vegetables on a conveyor belt for their movement to a packing machine with a robotic arm. The principal purpose of the algorithm is to identify the dimensions (length, width), and position (angle of inclination, and Cartesian coordinates) of the vegetable mesh netting without taking the product label into account. The proposed algorithm has four functions. A convolutive neuronal network model is applied for object detection, with which all objects are identified in the image while the product label is suppressed, so that recognition of the vegetable mesh netting and its dimensions is not impaired. Moreover, the Canny edge detector and the Border following algorithms are applied to perform image pre-processing and edge improvements, respectively, yielding optimal results with more clearly defined noise-free objects. The feature extraction function for dimensions yielded detection precision results of 99.60% for length, 99.00% for width, and 97.80% for inclination angle, showing optimal performance in the test phases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Improving Myanmar image Caption Generation Using NASNetLarge and Bi-directional LSTM

Improving Myanmar Image Caption Generation Using NASNetLarge...

引用

2023 IEEE conference on Computer applications, ICCA 2023

作者： Aung, San Pa Pa Pa, Win Pa Nwe, Tin Lay University of Computer Studies Natural Language Processing Lab Yangon Yangon Myanmar Institute for Infocomm Research Visual Intelligence Department Singapore Singapore

ISBN: (纸本)9781665435994

The main objective of this paper is to improve the automatic Myanmar captions by learning the contents of images using NASNetLarge and Bi-LSTM model. Describing the contents of an image is a complex task for machine without human intervention. Computer vision and Natural Language processing are widely used to tackle this problem. This paper proposed a deep learning-based Myanmar image captioning system which used a NASNetLarge feature extraction model of CNN as an encoder and a deep Recurrent Neural Network (RNN) with Bi-directional Long Short-Term Memory (LSTM) as a decoder. For corpus construction, we created and annotated the Myanmar image captions corpus (consists of over 40k Myanmar sentences), which is based on Flickr8k dataset. Furthermore, two different types of segmentations such as word segmentation level and syllable segmentation level are studied in text preprocessing step. In this work, the proposed Bi-directional LSTM model is compared with LSTM, GRU as well as the baseline model. Experiments on the updated dataset is presented that all of our models using syllable segmentation give higher and comparable BLEU scores than word segmentation for Myanmar image captioning system. NASNetLarge with Bi-directional LSTM model using syllable segmentation approach achieved the highest BLEU-4 score 40.05% which is 12.5% better than word segmentation in this work and 15.67% BLEU-4 score better than our previous work. © 2023 IEEE.

关键词： Long short-term memory

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Efficient Real-Time Okra Stage Identification using YOLOV8 10

Efficient Real-Time Okra Stage Identification using YOLOV8

引用

10th IEEE International conference on Electronics, Computing and Communication Technologies (IEEE CONECCT)

作者： Kumar, Nikhilesh B. Ferbin, F. J. Sivapatham, Shoba Kar, Asutosh Krithiga, R. Vellore Inst Technol Sch Mech Engn Chennai Tamil Nadu India Vellore Inst Technol Ctr Adv Data Sci Chennai Tamil Nadu India Natl Inst Technol Elect & Commun Engn Jalandhar Punjab India Vellore Inst Technol Sch Comp Sci Chennai Tamil Nadu India

ISBN: (纸本)9798350385939;9798350385922

In modern agriculture, crop growth monitoring is a crucial component, as it offers intuitive information about the health and growth of the plant, assisting farmers and other agricultural specialists. This systematic growth monitoring is necessary for crop health and agricultural productivity. We preferred the YOLOv8, which utilizes machine learning and offers efficient plant analysis in agriculture. This preferred method predicts bounding boxes and the probability of each possible class, allowing it to achieve exceptional detection speed without trading off accuracy. Pre-processing was done on the created, "Okra-dataset" to standardize the image to a fixed resolution and enhance our dataset's strength. We tested our work using different models: YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x. Our test revealed that YOLOv8x achieved the highest mean average precision (mAP) of 82.9%. The implementation of research indicates that YOLOv8x is a good tool for agricultural applications, which can be very helpful to farmers.

关键词： Crop growth monitoring computer vision YOLOv8

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Contribution Unveiling Cutting-Edge machine Learning Techniques for image Segmentation

Contribution Unveiling Cutting-Edge Machine Learning Techniq...

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International conference on Electrical and Electronics Engineering, ICEEE 2023

作者： Shaik, Nazeer Gupta, Ankur Bhati, Sunita Kumar, Jaideep Singh, Jagendra Budhiraja, Ishan Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology Andhra Pradesh Anantapur India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Applications JECRC University Jaipur India Raj Kumar Goel Institute of Technology AKTU University Lucknow India

ISBN: (纸本)9789819986606

Segregation of images is a critical step in processing images, computer vision, and a variety of other disciplines. The technique involves decomposing an illustration into numerous components or components, every single one that consists of an ensemble of elements with identical features for example African descent, frequency, or consistency. The most important objective of appearance, the intention of fragmentation seems to reduce complexity or customize the mathematical representation of an illustration in a way that is more readily reasonable and more straightforward for assessment. It is widely employed to locate boundaries and features in photos, and this is favorable in an assortment of industries including clinical imaging, object detection, recognition, and autonomous vehicles. Several image segmentation techniques are available, and in incorporating thresholding, zone is an area-based differentiation and corner-based recognition. A threshold segment is a simple and commonly used technique that involves setting a threshold value and dividing the pixels into two classes based on their intensity values. Region-based segmentation involves grouping pixels based on their spatial proximity and similarity in characteristics, while edge-based segmentation involves detecting edges or boundaries in an image and using them to separate different regions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image segmentation

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