Research on Detection of Floating Objects in River and Lake Based on AI Intelligent Image Recognition
- URL: http://arxiv.org/abs/2404.06883v2
- Date: Fri, 19 Apr 2024 06:07:22 GMT
- Title: Research on Detection of Floating Objects in River and Lake Based on AI Intelligent Image Recognition
- Authors: Jingyu Zhang, Ao Xiang, Yu Cheng, Qin Yang, Liyang Wang,
- Abstract summary: This study focuses on the detection of floating objects in river and lake environments, exploring an innovative approach based on deep learning.
The proposed system has demonstrated its ability to significantly enhance the accuracy and efficiency of debris detection, thus offering a new technological avenue for water quality monitoring in rivers and lakes.
- Score: 12.315852697312195
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With the rapid advancement of artificial intelligence technology, AI-enabled image recognition has emerged as a potent tool for addressing challenges in traditional environmental monitoring. This study focuses on the detection of floating objects in river and lake environments, exploring an innovative approach based on deep learning. By intricately analyzing the technical pathways for detecting static and dynamic features and considering the characteristics of river and lake debris, a comprehensive image acquisition and processing workflow has been developed. The study highlights the application and performance comparison of three mainstream deep learning models -SSD, Faster-RCNN, and YOLOv5- in debris identification. Additionally, a detection system for floating objects has been designed and implemented, encompassing both hardware platform construction and software framework development. Through rigorous experimental validation, the proposed system has demonstrated its ability to significantly enhance the accuracy and efficiency of debris detection, thus offering a new technological avenue for water quality monitoring in rivers and lakes
Related papers
- Towards an Autonomous Surface Vehicle Prototype for Artificial Intelligence Applications of Water Quality Monitoring [68.41400824104953]
This paper presents a vehicle prototype that addresses the use of Artificial Intelligence algorithms and enhanced sensing techniques for water quality monitoring.
The vehicle is fully equipped with high-quality sensors to measure water quality parameters and water depth.
By means of a stereo-camera, it also can detect and locate macro-plastics in real environments.
arXiv Detail & Related papers (2024-10-08T10:35:32Z) - Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future [119.88454942558485]
Underwater object detection (UOD) aims to identify and localise objects in underwater images or videos.
In recent years, artificial intelligence (AI) based methods, especially deep learning methods, have shown promising performance in UOD.
arXiv Detail & Related papers (2024-10-08T00:25:33Z) - FriendNet: Detection-Friendly Dehazing Network [24.372610892854283]
We propose an effective architecture that bridges image dehazing and object detection together via guidance information and task-driven learning.
FriendNet aims to deliver both high-quality perception and high detection capacity.
arXiv Detail & Related papers (2024-03-07T12:19:04Z) - FloodLense: A Framework for ChatGPT-based Real-time Flood Detection [0.0]
This study addresses the vital issue of real-time flood detection and management.
It innovatively combines advanced deep learning models with Large language models (LLM), enhancing flood monitoring and response capabilities.
arXiv Detail & Related papers (2024-01-27T20:52:33Z) - 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) - Semantic-aware Texture-Structure Feature Collaboration for Underwater
Image Enhancement [58.075720488942125]
Underwater image enhancement has become an attractive topic as a significant technology in marine engineering and aquatic robotics.
We develop an efficient and compact enhancement network in collaboration with a high-level semantic-aware pretrained model.
We also apply the proposed algorithm to the underwater salient object detection task to reveal the favorable semantic-aware ability for high-level vision tasks.
arXiv Detail & Related papers (2022-11-19T07:50:34Z) - Adversarially-Aware Robust Object Detector [85.10894272034135]
We propose a Robust Detector (RobustDet) based on adversarially-aware convolution to disentangle gradients for model learning on clean and adversarial images.
Our model effectively disentangles gradients and significantly enhances the detection robustness with maintaining the detection ability on clean images.
arXiv Detail & Related papers (2022-07-13T13:59:59Z) - Remote Sensing Image Classification using Transfer Learning and
Attention Based Deep Neural Network [59.86658316440461]
We propose a deep learning based framework for RSISC, which makes use of the transfer learning technique and multihead attention scheme.
The proposed deep learning framework is evaluated on the benchmark NWPU-RESISC45 dataset and achieves the best classification accuracy of 94.7%.
arXiv Detail & Related papers (2022-06-20T10:05:38Z) - A Generative Approach for Detection-driven Underwater Image Enhancement [19.957923413999673]
We present a model that integrates generative adversarial network (GAN)-based image enhancement with diver detection task.
Our proposed approach restructures the GAN objective function to include information from a pre-trained diver detector.
We train our network on a large dataset of scuba divers, using a state-of-the-art diver detector, and demonstrate its utility on images collected from oceanic explorations.
arXiv Detail & Related papers (2020-12-10T21:33:12Z)
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.