From Pixels to Plastic: Charting a Path to Detect, Track, and Count Oceanic Plastic Pollution through Computer Vision
Keywords: Plastic pollution in Oceans, Computer Vision, Object Detection, Object Tracking, Object counting
TL;DR: Detect, track, and count oceanic plastic pollution through computer vision
Abstract: Plastic pollution poses an alarming threat to marine ecosystems, necessitating innovative and efficient solutions for its monitoring and management.
Building upon recent advancements in Artificial Intelligence (AI), Machine
Learning (ML), and Deep Learning (DL) [7], we have developed a cuttingedge, AI-driven model utilizing the YOLOv8 architecture[4]. This model
not only excels in real-time detection of marine plastics but also integrates
advanced tracking and counting capabilities. Tailored for compatibility
with marine robotics and other low-resource applications, our approach
offers a robust solution even in GPU-deprived environments setting it apart
from previous efforts employing the R-CNN architecture[3]. Other recent
studies have employed imaging technologies coupled with deep learning
techniques, such as the deployment of bridge-mounted cameras on rivers
in Jakarta and the use of Unmanned Aerial Systems (UAS) for marine
litter mapping on beach-dune systems. Our model notably surpasses
these methodologies in performance, achieving superior precision, recall,
and overall efficiency metrics. Beyond its detection prowess, our model
represents a paradigm shift in the computational efficiency of monitoring
tools, poised to revolutionize the strategies to combat the plastic pollution
menace in aquatic ecosystems.
Keywords:Plastic pollution in Oceans,Computer Vision,Object
Detection,Object Tracking,Object counting
Submission Category: Machine learning algorithms
Submission Number: 37
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