Implementing Edge Based Object Detection For Microplastic Debris
- URL: http://arxiv.org/abs/2307.16289v1
- Date: Sun, 30 Jul 2023 17:55:03 GMT
- Title: Implementing Edge Based Object Detection For Microplastic Debris
- Authors: Amardeep Singh, Prof. Charles Jia, Prof. Donald Kirk
- Abstract summary: Plastic has imbibed itself as an indispensable part of our day to day activities.
Plastic debris levels continue to rise with the accumulation of waste in garbage patches in landfills.
The project has been able to produce workable models that can perform on time detection of sampled images.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Plastic has imbibed itself as an indispensable part of our day to day
activities, becoming a source of problems due to its non-biodegradable nature
and cheaper production prices. With these problems, comes the challenge of
mitigating and responding to the aftereffects of disposal or the lack of proper
disposal which leads to waste concentrating in locations and disturbing
ecosystems for both plants and animals. As plastic debris levels continue to
rise with the accumulation of waste in garbage patches in landfills and more
hazardously in natural water bodies, swift action is necessary to plug or cease
this flow. While manual sorting operations and detection can offer a solution,
they can be augmented using highly advanced computer imagery linked with
robotic appendages for removing wastes. The primary application of focus in
this report are the much-discussed Computer Vision and Open Vision which have
gained novelty for their light dependence on internet and ability to relay
information in remote areas. These applications can be applied to the creation
of edge-based mobility devices that can as a counter to the growing problem of
plastic debris in oceans and rivers, demanding little connectivity and still
offering the same results with reasonably timed maintenance. The principal
findings of this project cover the various methods that were tested and
deployed to detect waste in images, as well as comparing them against different
waste types. The project has been able to produce workable models that can
perform on time detection of sampled images using an augmented CNN approach.
Latter portions of the project have also achieved a better interpretation of
the necessary preprocessing steps required to arrive at the best accuracies,
including the best hardware for expanding waste detection studies to larger
environments.
Related papers
- Optimizing Waste Management with Advanced Object Detection for Garbage Classification [1.3499500088995462]
This paper reviews the implementation of AI models for classifying trash through object detection.
The study demonstrates how YOLO V5 can effectively identify various types of waste, including plastic, paper, glass, metal, cardboard, and biodegradables.
arXiv Detail & Related papers (2024-10-13T19:32:01Z) - SpectralWaste Dataset: Multimodal Data for Waste Sorting Automation [46.178512739789426]
We present SpectralWaste, the first dataset collected from an operational plastic waste sorting facility.
This dataset contains labels for several categories of objects that commonly appear in sorting plants.
We propose a pipeline employing different object segmentation architectures and evaluate the alternatives on our dataset.
arXiv Detail & Related papers (2024-03-26T18:39:38Z) - Solid Waste Detection, Monitoring and Mapping in Remote Sensing Images: A Survey [0.8499685241219366]
Improperly managed landfills contaminate soil and groundwater via infiltration rainwater, posing threats to both animals and humans.
Traditional landfill identification approaches, such as on-site inspections, are time-consuming and expensive.
Earth Observation (EO) satellites, equipped with an array of sensors and imaging capabilities, have been providing high-resolution data for several decades.
arXiv Detail & Related papers (2024-02-14T10:24:04Z) - Learning Heavily-Degraded Prior for Underwater Object Detection [59.5084433933765]
This paper seeks transferable prior knowledge from detector-friendly images.
It is based on statistical observations that, the heavily degraded regions of detector-friendly (DFUI) and underwater images have evident feature distribution gaps.
Our method with higher speeds and less parameters still performs better than transformer-based detectors.
arXiv Detail & Related papers (2023-08-24T12:32:46Z) - Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue
with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems.
This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
arXiv Detail & Related papers (2023-08-14T16:48:57Z) - A Vision for Cleaner Rivers: Harnessing Snapshot Hyperspectral Imaging
to Detect Macro-Plastic Litter [6.198237241838559]
Large parcels of plastic waste are transported from inland to oceans leading to a global scale problem of floating debris fields.
We analyze the feasibility of macro-plastic litter detection using computational imaging approaches in river-like scenarios.
arXiv Detail & Related papers (2023-07-22T18:59:27Z) - MWaste: A Deep Learning Approach to Manage Household Waste [0.0]
MWaste is a mobile application that uses computer vision and deep learning techniques to classify waste materials as trash, plastic, paper, metal, glass or cardboard.
Its effectiveness was tested on various neural network architectures and real-world images, achieving an average precision of 92% on the test set.
arXiv Detail & Related papers (2023-04-02T16:56:49Z) - VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting [61.52419223232737]
In industrial waste sorting, one of the biggest challenges is the extreme diversity of the input stream.
We present the VisDA 2022 Challenge on Domain Adaptation for Industrial Waste Sorting.
arXiv Detail & Related papers (2023-03-26T21:38:38Z) - Towards Generating Large Synthetic Phytoplankton Datasets for Efficient
Monitoring of Harmful Algal Blooms [77.25251419910205]
Harmful algal blooms (HABs) cause significant fish deaths in aquaculture farms.
Currently, the standard method to enumerate harmful algae and other phytoplankton is to manually observe and count them under a microscope.
We employ Generative Adversarial Networks (GANs) to generate synthetic images.
arXiv Detail & Related papers (2022-08-03T20:15:55Z) - ZeroWaste Dataset: Towards Automated Waste Recycling [51.053682077915546]
We present the first in-the-wild industrial-grade waste detection and segmentation dataset, ZeroWaste.
This dataset contains over1800fully segmented video frames collected from a real waste sorting plant.
We show that state-of-the-art segmentation methods struggle to correctly detect and classify target objects.
arXiv Detail & Related papers (2021-06-04T22:17:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.