Mitigating marine litter challenge with Image Data Annotation & Machine Learning Solution
Mitigating marine litter challenge with Image Data Annotation & ML
Marine litter presents a significant environmental challenge, endangering marine ecosystems. Our project addresses this issue through advanced image annotation techniques, combining polygon and bounding box methods to create a robust dataset essential for research and conservation efforts. s
Benefits of VE3 Image Annotation Solutions
ML Solution
By training ML algorithm with datasets, the marine litter can be actively monitored and mitigated
Data Security and Compliance
Ensured the safety of data, we stored and access it in compliance with the specified preferences.
Support for Research and Conservation
Our dataset is now a valuable resource for researchers and conservationists working to combat marine litter.
VE3 Image Annotation Solutions
We tackled the challenge of marine litter using advanced image annotation techniques, utilizing both polygon and bounding box annotation, timely delivering a robust dataset to train ML algo & support conservation efforts.
Annotation Process
We used polygon and bounding box annotations for comprehensive and accurate marine litter identification. COCO JSON format annotations were provided for seamless integration into research.
Quality Assurance
We implemented a rigorous quality assurance pipeline to detect and correct annotation errors, ensuring high-quality data. Inter-rater assessments were conducted at key project stages which enhanced annotation consistency.
Technology and Data Security
Data was securely stored and accessed according to specified preferences. Globally accepted annotation formats and ISO 9001 quality management were maintained.
Timely Delivery
As part of our commitment to excellence, we ensured that all 7,000 annotated images were meticulously processed and delivered within the agreed-upon timelines.
Collaboration and Compliance
We ensured local experts were engaged to understand the marine litter landscape. Strict adherence to OSPAR guidelines and regulations was ensured.
Environmental Considerations
Environmentally responsible practices were integrated throughout the project. Staff, suppliers, and the community were influenced to support environmental protection.
Data Quality Metrics
We prioritize data accuracy through rigorous quality assurance (QA). Our QA process includes metrics like inter-annotator agreement (IAA), precision, recall, and F1-score to ensure high-quality annotations.
Detailed Annotation Methodology
We employ a detailed annotation process using polygon and bounding box annotations for precise marine litter identification. Advanced annotation tools optimize efficiency and
quality control.
ML Model Performance
Leveraging the annotated dataset, we train machine learning models to achieve over 90% accuracy in identifying 89 marine litter categories, demonstrating the effectiveness of our annotation approach.
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VE3 Success Stories in Image Annotation Solutions
VE3 partnered with the Centre for Environment, Fisheries and Aquaculture Science (Cefas) to develop a machine learning algorithm capable of identifying at least 89 marine litter items based on international protocols. To achieve this, approximately 7,000 high-resolution images from beaches were annotated.
Insights
Read our insights, where we delve into the implementation of image annotation to annotate high-resolution images of marine litter in the ocean. The role ML plays in environmental monitoring and conservation.
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