Implementation of Image Processing and Machine Learning in High Resolution Aerial Image Datasets for Lake Resource Usage, Aquaculture, and Coastal Community

Last May 2019, fish farms in Taal Lake suffer from fish kill resulting in an estimated loss of 405 tons of fish. According to the report of BFAR, the measured water sample from the affected areas shows depletion of dissolved-oxygen level that caused the fish kill. DENR stated that the triggering factor of the oxygen level deterioration is the over-crowding of fish cages. Recent studies utilize satellite remote-sensors to map and monitor the aquaculture inside the lake. The maps are being used as reference material for progress monitoring, as decision-support and lake management tool by the local government and regulatory agencies. With the advent of Unmanned Aerial Vehicle (UAV) technology, aerial images can be captured with higher resolution and much lower cost compared to satellite imagery. This study makes use of Ateneo Innovation Center high resolution aerial image datasets to create segmentation model for aquaculture structures and coastal settlements. The image dataset was sliced and annotated then fed into the training process to generate a detection model. This study implemented Mask Regional Convolutional Neural Network (Mask RCNN) as machine learning framework to detect and segment the desired artificial geographic objects (aquaculture and roof). After training and validation processes, the method resulted to detection and segmentation of aquaculture structures and coastal settlements. Finally, an analytical software was developed to utilize segmented maps for zone management plan implementation, lake resource usage calculation, and gauge the population of settlers along the coastline. This provides meaningful visual and statistical data regarding aquaculture population, lake resource usage, local settlement population and zone development plan status

Implementation of Image Processing and Machine Learning in High Resolution Aerial Image Datasets for Lake Resource Usage, Aquaculture, and Coastal Community

Last May 2019, fish farms in Taal Lake suffer from fish kill resulting in an estimated loss of 405 tons of fish. It was reported that the measured water sample from the lake shows significant loss of dissolved-oxygen due to over-crowding of fish farm. With the crisis mentioned, recent studies utilize satellite remote sensors to map and monitor the aquaculture inside the lake. The maps are being used as reference material for progress monitoring, as decision-support and lake management tool by the local government and regulatory agencies. Aerial maps were captured using Unmanned Aerial Vehicle (UAV) as it has better resolution than satellite imagery. This study implements image processing and Mask Regional Convolutional Neural Network (Mask RCNN) on high resolution images to create an object detection and segmentation model for aquaculture structures and coastal settlement. To create the detection model, the image dataset undergoes preprocessing before feeding into the training process. Finally, an analytical software was developed to utilize segmented maps for zone management plan implementation, lake resource usage calculation, and gauge the population of settlers along the coastline. This provides meaningful visual and statistical data regarding aquaculture population, lake resource usage, local settlement population and zone development plan status

Design of a Breach Detection System for Social Distancing

The pandemic caused by the 2019 novel coronavirus introduced essential health protocols for everyone\u27s safety. One of which is maintaining a social distance of at least 1 meter as per the guideline set by World Health Organization (WHO). Currently, most spaces were designed prior to the implementation of the social/physical distancing protocol. This project aims to design and develop a detection system utilizing closed-circuit television cameras, to identify spaces where there is a possible breach in the social distancing protocol. The system will generate discrete data to be queried for tabulation, and analysis. The system will also generate a breach map, which indicates the area in the CCTV footage where increasing breaches occur and are marked in increasing color intensity. The system utilized the YOLO V3 object detection algorithm in identifying an object to be human. The system utilized perspective transformation and Euclidean distance estimation in approximating distance for the social distancing protocol. In summary, the human detection accuracy of the system is ≃ 91%, processing at a rate of 30 frames per second in real-time

Design of a Breach Detection System for Social Distancing

The pandemic caused by the 2019 novel coronavirus introduced essential health protocols for everyone\u27s safety. One of which is maintaining a social distance of at least 1 meter as per the guideline set by World Health Organization (WHO). Currently, most spaces were designed prior to the implementation of the social/physical distancing protocol. This project aims to design and develop a detection system utilizing closed-circuit television cameras, to identify spaces where there is a possible breach in the social distancing protocol. The system will generate discrete data to be queried for tabulation, and analysis. The system will also generate a breach map, which indicates the area in the CCTV footage where increasing breaches occur and are marked in increasing color intensity. The system utilized the YOLO V3 object detection algorithm in identifying an object to be human. The system utilized perspective transformation and Euclidean distance estimation in approximating distance for the social distancing protocol. In summary, the human detection accuracy of the system is ≃ 91%, processing at a rate of 30 frames per second in real-time

Multiple Edge Computing Devices with Computer Vision for Social Distancing

Coronavirus disease, widely known as COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. Once infected, a person can spread the virus through their nose or mouth in small particles when they cough, sneeze, speak, or breathe. According to the World Health Organization (WHO), one way to be protected from the risk of virus infection is to stay at least 1 meter apart from others while wearing a properly filtered mask. The study aims to design and develop a multiple edge computing system with computer vision capabilities to monitor the adherence of social distancing in multiple locations and in real time. An edge computing device uses a camera to process a stream of images. Graphical Processing Unit (GPU) was utilized for faster inference processing to detect people. The person\u27s location will undergo transformation to get a 2D perspective. Then, a distance calculation algorithm will be imposed to each pair of persons detected to detect breach of social distancing protocol. For every breach detected, location coordinates will be sent to the host database for visualization and monitoring. The use of multiple edge computing devices for computer vision application was compared to the IP camera system in monitoring multiple locations. It is found that utilization of multiple edge computing devices has significant advantages in terms of power consumption, data acquisition, image processing and inference, and setup cost

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)