scholarly journals A Citizen Science Unmanned Aerial System Data Acquisition Protocol and Deep Learning Techniques for the Automatic Detection and Mapping of Marine Litter Concentrations in the Coastal Zone

Drones ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 6
Author(s):  
Apostolos Papakonstantinou ◽  
Marios Batsaris ◽  
Spyros Spondylidis ◽  
Konstantinos Topouzelis
Keyword(s):  
Deep Learning ◽  
Data Acquisition ◽  
Citizen Science ◽  
Coastal Zone ◽  
Automatic Detection ◽  
Monitoring Methods ◽  
Monitoring Method ◽  
Marine Litter ◽  
Density Maps ◽  
Learning Techniques

Marine litter (ML) accumulation in the coastal zone has been recognized as a major problem in our time, as it can dramatically affect the environment, marine ecosystems, and coastal communities. Existing monitoring methods fail to respond to the spatiotemporal changes and dynamics of ML concentrations. Recent works showed that unmanned aerial systems (UAS), along with computer vision methods, provide a feasible alternative for ML monitoring. In this context, we proposed a citizen science UAS data acquisition and annotation protocol combined with deep learning techniques for the automatic detection and mapping of ML concentrations in the coastal zone. Five convolutional neural networks (CNNs) were trained to classify UAS image tiles into two classes: (a) litter and (b) no litter. Testing the CCNs’ generalization ability to an unseen dataset, we found that the VVG19 CNN returned an overall accuracy of 77.6% and an f-score of 77.42%. ML density maps were created using the automated classification results. They were compared with those produced by a manual screening classification proving our approach’s geographical transferability to new and unknown beaches. Although ML recognition is still a challenging task, this study provides evidence about the feasibility of using a citizen science UAS-based monitoring method in combination with deep learning techniques for the quantification of the ML load in the coastal zone using density maps.

Automatic detection of seafloor marine litter using towed camera images and deep learning

2021 ◽  
Vol 164 ◽  
pp. 111974
Author(s):  
Dimitris V. Politikos ◽  
Elias Fakiris ◽  
Athanasios Davvetas ◽  
Iraklis A. Klampanos ◽  
George Papatheodorou
Keyword(s):  
Deep Learning ◽  
Automatic Detection ◽  
Marine Litter

Deep Learning Techniques for Diabetic Retinopathy Detection

2020 ◽  
Vol 16 ◽  
Author(s):  
Sehrish Qummar ◽  
Fiaz Gul Khan ◽  
Sajid Shah ◽  
Ahmad Khan ◽  
Ahmad Din ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Blood Vessels ◽  
Blood Sugar ◽  
Automatic Detection ◽  
Multiclass Classification ◽  
The Body ◽  
Future Directions ◽  
Learning Techniques

Diabetes occurs due to the excess of glucose in the blood that may affect many organs of the body. The increase in blood sugar in the body causes many problems. One of the most prominent of these problems is Diabetic Retinopathy (DR). DR occurs due to the mutilation of the blood vessels in a retina. The detection of DR is complicated and time-consuming due to its features for the ophthalmologists. Therefore, automatic detection is required, recently different machine and deep learning techniques are being applied to detect and classify DR. In this paper, we conducted a study of the various techniques available in the literature for the identification/classification of DR, the datasets used, strengths and weaknesses of each method and provides the future directions. Moreover, we also discussed the different steps for the detection that are segmentation of blood vessels in a retina, detecting lesions and other abnormalities of DR in binary and multiclass classification.

scholarly journals Unreliable Users Detection in Social Media: Deep Learning Techniques for Automatic Detection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 213154-213167
Author(s):  
Giuseppe Sansonetti ◽  
Fabio Gasparetti ◽  
Giuseppe D'aniello ◽  
Alessandro Micarelli
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Automatic Detection ◽  
Learning Techniques

scholarly journals Improving the Accuracy of Species Identification by Combining Deep Learning With Field Occurrence Records

2021 ◽  
Vol 9 ◽  
Author(s):  
Jianqiang Sun ◽  
Ryo Futahashi ◽  
Takehiko Yamanaka
Keyword(s):  
Deep Learning ◽  
Species Identification ◽  
Citizen Science ◽  
Automated System ◽  
Identification System ◽  
Effective Strategy ◽  
Web Scraping ◽  
Learning Techniques ◽  
Scanned Images ◽  
Occurrence Records

Citizen science is essential for nationwide ecological surveys of species distribution. While the accuracy of the information collected by beginner participants is not guaranteed, it is important to develop an automated system to assist species identification. Deep learning techniques for image recognition have been successfully applied in many fields and may contribute to species identification. However, deep learning techniques have not been utilized in ecological surveys of citizen science, because they require the collection of a large number of images, which is time-consuming and labor-intensive. To counter these issues, we propose a simple and effective strategy to construct species identification systems using fewer images. As an example, we collected 4,571 images of 204 species of Japanese dragonflies and damselflies from open-access websites (i.e., web scraping) and scanned 4,005 images from books and specimens for species identification. In addition, we obtained field occurrence records (i.e., range of distribution) of all species of dragonflies and damselflies from the National Biodiversity Center, Japan. Using the images and records, we developed a species identification system for Japanese dragonflies and damselflies. We validated that the accuracy of the species identification system was improved by combining web-scraped and scanned images; the top-1 accuracy of the system was 0.324 when trained using only web-scraped images, whereas it improved to 0.546 when trained using both web-scraped and scanned images. In addition, the combination of images and field occurrence records further improved the top-1 accuracy to 0.668. The values of top-3 accuracy under the three conditions were 0.565, 0.768, and 0.873, respectively. Thus, combining images with field occurrence records markedly improved the accuracy of the species identification system. The strategy of species identification proposed in this study can be applied to any group of organisms. Furthermore, it has the potential to strike a balance between continuously recruiting beginner participants and updating the data accuracy of citizen science.

Towards a coastal marine litter observatory with combination of drone imagery, artificial intelligence, and citizen science

2021 ◽  
Author(s):  
Konstantinos Topouzelis ◽  
Apostolos Papakonstantinou ◽  
Marios Batsaris ◽  
Ioannis Moutzouris ◽  
Spyros Spondylidis ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Citizen Science ◽  
Spatial Data ◽  
Aerial Images ◽  
Automatic Identification ◽  
Plastic Pollution ◽  
Marine Litter ◽  
Data Infrastructure ◽  
Coastal Marine ◽  
Density Maps

<p>The presence of plastic litters in the coastal zone has been recognized as a significant problem. It can dramatically affect flora and fauna and lead to severe economic impacts on coastal communities, tourism and fishing industries. Traditional beach litter reports include individual transects on the beach, reporting on the litter's presence through a dedicated measuring protocol. In the new era of drone imagery, a new integrated coastal marine litter observatory is proposed. This observatory is based on aerial images acquired through citizen science using low cost self-owned drones and the automatic identification of litter accumulation zones through computer vision. The methodology consists of four steps: i) a dedicated protocol for acquiring drone imagery from non-experienced citizens using commercial drones, ii) image pre-processing (image tiling and geo-enrichment) and crowdsourced annotation, iii) data classification to litter and no litter though an artificial intelligence classification approach and iv) marine litter density maps creation and reporting. The resulted density maps currently are produced calculating the tiles containing litter at areas of hundred square meters on the beach and the entire process requires some minutes to run once the aerial data is uploaded online. The density maps automatically are reported to a spatial data infrastructure, ideal for time series analysis. Classification accuracy calculated against manual identification of 77.6%. The coastal marine litter observatory presents several benefits against traditional reporting methods, i.e. improved measurement of the policies against plastic pollution, validating marine litter transportation models, monitoring the SDG Indicator 14.1.1, and most important, guiding the cleaning efforts towards areas with a significant amount of litter.</p>

Automatic Meter Reading using Deep Learning

2021 ◽  
Author(s):  
S.H. Wong ◽  
◽  
S.L. Yang ◽  
C.M. Tsui
Keyword(s):  
Deep Learning ◽  
Data Acquisition ◽  
Human Errors ◽  
Improve Efficiency ◽  
Automatic Meter Reading ◽  
Learning Techniques ◽  
Automatic Meter Reading System ◽  
Reading System

Laboratory instruments are commonly equipped with communication interfaces (e.g., GPIB, USB or LAN port) for data acquisition or control through a computer. However, such interface might not be available on handheld equipment, e.g., multi-meters, where readings have to be taken manually by operators during calibration. To improve efficiency and reduce possible human errors, SCL has developed an automatic meter reading system for seven segment displays using deep learning techniques.

scholarly journals Scale, origin and spatial distribution of marine litter pollution in the Lithuanian coastal zone of the Baltic Sea

Baltica ◽  
2014 ◽  
Vol 27 (special) ◽  
pp. 39-44 ◽  
Author(s):  
Arūnas Balčiūnas ◽  
Nerijus Blažauskas
Keyword(s):  
Baltic Sea ◽  
Coastal Zone ◽  
Marine Pollution ◽  
Ecological Status ◽  
Pollution Level ◽  
Monitoring Methods ◽  
The Baltic Sea ◽  
Dominant Type ◽  
Marine Litter ◽  
The Baltic

The first attempt to investigate the marine litter pollution level of the Lithuanian coastal zone was carried out based on different marine litter monitoring methods and according to the lists of identifiable items. The results have proven that plastic is the dominant type of marine litter. It seems that tourism and fishery related marine litter occurrence do not significantly depend on seasonal variations. The outcome of the study will serve as basic information for future inventory of the character of marine pollution, provide the scientifically grounded limit value for Good Environmental Status (GES) assessment in the Lithuanian coastal zone, and will contribute for the enhancement of the ecological status of the south–eastern coasts of the Baltic Sea.

Anomaly Detection and Categorization in Cloud Environment using Deep Learning Techniques

2019 ◽  
Vol 7 (5) ◽  
pp. 211-214
Author(s):  
Nidhi Thakkar ◽  
Miren Karamta ◽  
Seema Joshi ◽  
M. B. Potdar
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Cloud Environment ◽  
Learning Techniques

Deep Learning Techniques for Naskh and Nastalique Writing Style Text Recognition

2019 ◽  
Vol 7 (4) ◽  
pp. 70-76
Author(s):  
Shanky Goel ◽  
Gurpreet Singh Lehal
Keyword(s):  
Deep Learning ◽  
Text Recognition ◽  
Writing Style ◽  
Learning Techniques
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