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Automatic Detection of Nephrops Norvegicus Burrows from Underwater Imagery Using Deep Learning

Computers, Materials & Continua 2022年第3期70卷 5321-5344页

作者： atif naseer Enrique Nava Baro Sultan Daud Khan Yolanda Vila Jennifer Doyle ETSI Telecomunicación Universidad deMálagaMálaga29071Spain Department of Computer Science National University of TechnologyIslamabad44000Pakistan Instituto Espanol de Oceanografía Centro Oceanográfico de CádizCádiz39004Spain Marine Institute Rinville OranmoreIreland

The Norway lobster,Nephrops norvegicus,is one of the main commercial crustacean fisheries in *** abundance of Nephrops norvegicus stocks is assessed based on identifying and counting the burrows where they live from underwater videos collected by camera systems mounted on *** Spanish Oceanographic Institute(IEO)andMarine Institute Ireland(MIIreland)conducts annual underwater television surveys(UWTV)to estimate the total abundance of Nephrops within the specified area,with a coefficient of variation(CV)or relative standard error of less than 20%.Currently,the identification and counting of the Nephrops burrows are carried out manually by the marine *** is quite a time-consuming *** a solution,we propose an automated system based on deep neural networks that automatically detects and counts the Nephrops burrows in video footage with high *** proposed system introduces a deep-learning-based automated way to identify and classify the Nephrops *** research work uses the current state-of-the-art Faster RCNN models Inceptionv2 and MobileNetv2 for object detection and *** conduct experiments on two data sets,namely,the Smalls Nephrops survey(FU 22)and Cadiz Nephrops survey(FU 30),collected by Marine Institute Ireland and Spanish Oceanographic Institute,*** the results,we observe that the Inception model achieved a higher precision and recall rate than *** best mean Average Precision(mAP)recorded by the Inception model is 81.61%compared to MobileNet,which achieves the best mAP of 75.12%.

关键词： Faster RCNN computer vision nephrops norvegicus nephrops norvegicus stock assessment underwater videos classification

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Deep Trajectory Classification Model for Congestion Detection in Human Crowds

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Computers, Materials & Continua 2021年第7期68卷 705-725页

作者： Emad Felemban Sultan Daud Khan atif naseer Faizan Ur Rehman Saleh Basalamah Department of Computer Engineering College of Computing and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science National University of TechnologyIslamabadPakistan Science and Technology Unit Umm Al-Qura UniversityMakkahSaudi Arabia Institute of Consulting Research and Studies Umm Al-Qura UniversityMakkahSaudi Arabia

In high-density gatherings,crowd disasters frequently occur despite all the safety *** detection of congestion in human crowds using automated analysis of video footage can prevent crowd *** work on the prevention of crowd disasters has been based on manual analysis of video *** methods also measure crowd congestion by estimating crowd ***,crowd density alone cannot provide reliable information about *** paper proposes a deep learning framework for automated crowd congestion detection that leverages pedestrian *** proposed framework divided the input video into several temporal *** then extracted dense trajectories from each temporal segment and converted these into a spatio-temporal image without losing information.A classification model based on convolutional neural networks was then trained using spatio-temporal ***,we generated a score map by encoding each point trajectory with its respective class *** this,we obtained the congested regions by employing the non-maximum suppression method on the score ***,we demonstrated the proposed framework’s effectiveness by performing a series of experiments on challenging video sequences.

关键词： Crowd congestion trajectory classification crowd analysis

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Appraisal of Deforestation in Murree through Open Source Satellite Imagery

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Advances in Remote Sensing 2018年第2期7卷 61-70页

作者： Tabasam Jamal Shoaib naseer Syed Shehzad Hassan Hafsa Batool Rashid Mahmood Attia Naz atif Butt Usman Tanver Imran Saddique Kaukab Sana Alvi Javed Ahmad Azeem Akhtar Arshid Javed A. Ali Department of Geography University of the Punjab New Campus Lahore Punjab Pakistan Remote Sensing Group Department of Space Science University of the Punjab Lahore Pakistan

Forests are the main source of income for humans and shelter for many rare in habitants. Forests act as carbon sink and help to reduce the effect of natural hazards like floods. The timber is among the main products of forests used for precious furniture. Forest must cover one third of the part of any country but in Pakistan, it covers less than 4%. If the deforestation continues with the same ratio, there will be a drastic change in the upcoming years. The main objective of the study is to determine deforestation rates in Murree for the years 1999-2015 through open source Landsat 7, 8 imageries. The areas with high deforestation rates are also delineated. Supervised classification is used to delineate the area under buildup, vegetation and water that was 83.85 km2, 760.63 km2 and 1.18 km2 respectively in 1999. Built up area increased up to 20% and forests declined by 23% in 2015 as compared to 1999. Remote sensing and GIS tools helped to investigate the deforestation rates with high accuracy.

关键词： Landsat 7 Landsat 8 Supervised Classification Deforestation Vegetation Index

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