College of Science and Mathematics

Vegetable Juice Beverage, 2023

Written by Rene Estremera. Posted in News

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The Technology Transfer & Business Development Office (TTBDO) hosted a demonstration on February 6, 2023, by Ms. Diane Rose G. Castillon (in photo below) and Dr. Dann Marie N. Del Mundo (Dept. of Food Science & Chemistry) for a technology developed for a juice beverage (not from concentrate) with the use of nutritious vegetables that are locally available in the Philippines. According to the demonstrators, this juice product could offer a more affordable option with the same benefit as that of juice blends in the market, making it more acceptable to the local market. 
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"We are excited by the possibilities that this research could bring," said TTBDO head Lynda Buenobra. At the conclusion of the demonstration, the team identified several opportunities to further improve the quality of the product. "We aim that this research will be successful in delivering a more accessible and nutritious beverage option to the local market," said Prof. Del Mundo.

Publication: Combining Image Enhancement Techniques

Written by Rene Estremera. Posted in News

Combining Image Enhancement Techniques and Deep Learning for Shallow Water Benthic Marine Litter Detection
 
 

Authors: Gil Emmanuel Bancud, Alex John Labanon, Neil Angelo Abreo & Vladimer Kobayashi
Conference paper
First Online: 31 January 2023
Part of the Communications in Computer and Information Science book series (CCIS,volume 1752)

Abstract


The scarcity of information about benthic marine litter especially in developing countries hampers the implementation of targeted actions to minimize the extent of its impacts. This study developed a system using image processing and deep learning methods for detecting/tracking marine macro litter that can efficiently identify and quantify its amount in benthic environments in shallow coastal areas. Shallow underwater litter detection poses several challenges. First is the low quality of images. Second is the difficulty in recognizing litter brought by their varying visual characteristics. Third is the lack of available data for training. Underwater images of litter were collected from marine litter hotspots in coastal areas in southern Philippines. This study experimented with various object detection algorithms. The best object detection model is then paired with various image enhancement techniques to determine the optimal combination. Among the combinations that were tested, YOLOv5n combined with CLAHE gave the best performance for simple binary task (litter or not litter) with a mAP@0.5 of 0.704. Furthermore, the results showed that applying underwater image enhancement techniques provides noticeable improvement for object detection models on detecting marine litter.

Keywords: Yolov5, Image enhancement, Marine litter, Object detection

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