Underwater Object Detection Using Python
Budget: ₹600 – ₹1,500 INR
Project Overview: Underwater Object Detection System(python)
We aim to develop an advanced underwater object detection system capable of identifying, categorizing, and visualizing various objects present in underwater images. The system will use state-of-the-art techniques to detect objects, categorize them into distinct classes, and display these objects within bounding boxes, labeled with their respective categories.
Advanced Detection with YOLOv8:
The core of our system will leverage the YOLOv8 object detection model, known for its high accuracy and real-time processing capabilities.
The model will be trained on a custom dataset specific to underwater environments to enhance detection precision.
Detailed Performance Metrics:
The system will display key performance metrics such as precision, accuracy, F-score, and recall, providing a comprehensive evaluation of the model's effectiveness.
Users will also be able to view precision-recall curves for a more in-depth understanding of the model's performance.
Annotated Visualization:
After processing, the system will generate a result image with clearly marked bounding boxes around detected objects, each annotated with its predicted category label.
Implementation Plan:
Model Integration: Incorporate the YOLOv8 model for object detection, trained on the provided dataset to ensure optimized results.
Performance Evaluation: Implement modules to calculate and display precision, accuracy, F-score, recall metrics, and their respective curves.
Result Visualization: Generate and display the annotated image with bounding boxes around detected objects, categorized appropriately.
For further reference, a research paper has been provided, which outlines the methodologies and techniques relevant to this project.
dataset is here -https://github.com/Neeleshnama/BTP_UnderWaterObjectDetection/tree/main/data
We aim to develop an advanced underwater object detection system capable of identifying, categorizing, and visualizing various objects present in underwater images. The system will use state-of-the-art techniques to detect objects, categorize them into distinct classes, and display these objects within bounding boxes, labeled with their respective categories.
Advanced Detection with YOLOv8:
The core of our system will leverage the YOLOv8 object detection model, known for its high accuracy and real-time processing capabilities.
The model will be trained on a custom dataset specific to underwater environments to enhance detection precision.
Detailed Performance Metrics:
The system will display key performance metrics such as precision, accuracy, F-score, and recall, providing a comprehensive evaluation of the model's effectiveness.
Users will also be able to view precision-recall curves for a more in-depth understanding of the model's performance.
Annotated Visualization:
After processing, the system will generate a result image with clearly marked bounding boxes around detected objects, each annotated with its predicted category label.
Implementation Plan:
Model Integration: Incorporate the YOLOv8 model for object detection, trained on the provided dataset to ensure optimized results.
Performance Evaluation: Implement modules to calculate and display precision, accuracy, F-score, recall metrics, and their respective curves.
Result Visualization: Generate and display the annotated image with bounding boxes around detected objects, categorized appropriately.
For further reference, a research paper has been provided, which outlines the methodologies and techniques relevant to this project.
dataset is here -https://github.com/Neeleshnama/BTP_UnderWaterObjectDetection/tree/main/data