Summary of Deep Learning Innovations For Underwater Waste Detection: An In-depth Analysis, by Jaskaran Singh Walia and Pavithra L K
Deep Learning Innovations for Underwater Waste Detection: An In-Depth Analysis
by Jaskaran Singh Walia, Pavithra L K
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the pressing issue of submerged underwater trash detection, which is crucial for preserving marine life and aquatic ecosystems. The challenge lies in assessing waste beneath the surface due to image distortions caused by factors like light refraction, suspended particles, and color shifts. To address this gap, the authors conduct a comprehensive review of state-of-the-art architectures and datasets to establish a baseline for submerged waste detection. They aim to benchmark object localization techniques for use with advanced underwater sensors and autonomous vehicles. The ultimate goal is to explore and remove underwater debris. The paper highlights the need for robust algorithmic solutions due to the absence of benchmarks in many researches. This study provides a performance comparison analysis of various underwater trash detection algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding ways to detect and clean up trash that’s hidden under the ocean. Right now, it’s hard to do because of how light behaves when it hits water, making it hard to see what’s down there. The researchers are looking for a way to make it easier by reviewing the best methods and tools used so far. They want to create a standard method to help sensors and robots find and remove trash from the ocean floor. The problem is that many research studies don’t have any standards, making it hard to compare results. This study tries to change that by comparing different methods for detecting underwater trash. |
