A Generative Approach Towards Improved Robotic Detection of Marine Litter

Hong, Jungseok; Fulton, Michael S.; Sattar, Junaed

doi:10.1109/icra40945.2020.9197575

Cited by 22 publications

(17 citation statements)

References 15 publications

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“…In this paper, the TrashCan dataset, 37 a publicly available dataset of underwater trash processed by Sovit Rath in November 2022, is used to validate the performance of UTD-YOLO. Images in TrashCan are from the J-EDI (JAMSTEC E-Library of Deep-Sea Images) dataset.…”

Section: Experiments and Resultsmentioning

confidence: 99%

UTD-YOLO: underwater trash detection model based on improved YOLOv5

Wu,

Ge,

Yang

2023

J. Electron. Imag.

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Section: Experiments and Resultsmentioning

confidence: 99%

UTD-YOLO: underwater trash detection model based on improved YOLOv5

Wu,

Ge,

Yang

2023

J. Electron. Imag.

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“…Due to the scarcity of trash data, for the purpose of data regeneration, [78] make use of both the two-stage variational autoencoder (VAE) and the binary classifier (augumentation). An evaluation of the effect that the augmentation procedure has is carried out with the use of a multi-class classifier.…”

Section: B Waste Classificationmentioning

confidence: 99%

A Survey on Waste Detection and Classification Using Deep Learning

Abdu

Noor

2022

IEEE Access

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Waste or trash management is receiving increased attention for intelligent and sustainable development, particularly in developed and developing countries. The waste or trash management system comprises several related processes that carry out various complex functions. Recently, interest in deep learning (DL) has increased in providing alternative computational techniques for determining the solution to various waste or trash management problems. Researchers have concentrated on this domain, and as a result, significant research has been published, particularly in recent years. According to the literature, a few comprehensive surveys have been done on waste detection and classification. However, no study has investigated the application of DL to solve waste or trash management problems in various domains and highlight the available datasets for waste detection and classification in different domains. To this end, this survey contributes by reviewing various image classification and object detection models, and their applications in waste detection and classification problems, providing an analysis of waste detection and classification techniques with precise and organized representation and compiling over twenty benchmarked trash datasets. Also, we backed up the study with the challenges of existing methods and the future potential in this field. This will give researchers in this area a solid background and knowledge of the state-of-the-art deep learning models and insight into the research areas that can still be explored.

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“…All samples from one image may be correlated. Because the network may take a long time to reach convergence, Mask R-CNN can return a mask for each detected object [16,17].…”

Section: Faster Region Convolutional Neural Network (Faster R-cnn)mentioning

confidence: 99%

Faster R-CNN Algorithm for Detection of Plastic Garbage in the Ocean: A Case for Turtle Preservation

Faisal¹,

Chaudhury

Sankaran

et al. 2022

Mathematical Problems in Engineering

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Turtles are one of the ancient marine animals that live today. However, the population is threatened with extinction, so its existence needs to be protected and preserved because turtles often eat plastic waste in the ocean whose shape, texture, and color are similar to jellyfish. The technology in the computer vision area can be used to find the solution related to the case of reducing plastics and bottles trash in the ocean by implementing robotics. The region-based Convolutional Neural Network (CNN) is the latest image segmentation and has good detection accuracy based on the Faster R-CNN algorithm. In this study, the training image was built based on two different objects, namely plastic bottles and plastic bags. The target is that the two objects can be recognized even if there are other objects in the vicinity, or the image quality will be affected by the color of the seawater. The results obtained are that plastic objects and bottles can be recognized correctly in the picture. Of the five-color hues tested, the results show that the object detection process is valid on the average color hue, sepia, bandicoot, and grayscale. In contrast, the object detection process is invalid in black-and-white tones. The test results shown in the table explain that the object detection that gets the highest results is an image with normal coloring, while the lowest value is on bandicoot. The average accuracy of all types of images tested is 96.50. However, the accuracy value still needs to be improved to apply feasibility permanently to hardware such as diving robots.

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A Generative Approach Towards Improved Robotic Detection of Marine Litter

Cited by 22 publications

References 15 publications

UTD-YOLO: underwater trash detection model based on improved YOLOv5

UTD-YOLO: underwater trash detection model based on improved YOLOv5

A Survey on Waste Detection and Classification Using Deep Learning

Faster R-CNN Algorithm for Detection of Plastic Garbage in the Ocean: A Case for Turtle Preservation

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