Sea Trash Detection And Cleanup System

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@dushyant317

Hello, I’ve carefully reviewed your project requirements and clearly understand the tasks involved. I have 13 years of experience and strong expertise in the exact skills this project requires. I have successfully delivered similar projects before and can share relevant samples if needed. I will complete this within your expected timeline while maintaining quality and clear communication. I look forward to working with you and contributing sincerely to your project’s success.

@syedabdullahh7

Hi, I’m AI/ML Developer specializing in Agentic AI systems and intelligent automation. A CNN model will be trained on provided dataset and then marine objects will be identified by the model. Location will be extracted from Image metadata or any location mark stored by AUVs/ROVs. I have previously worked with object detection models like YOLO. PRICE NEGOTIABLE ➡️ Fast delivery | 100% satisfaction

@mudassirs52

✔ I deliver 100% accuracy — experimental or unstable results are not acceptable to me. ✔ Workflow Diagram Dataset Collection ⟶⟶ Data Cleaning & Augmentation ⟶⟶ CNN Model Training ⟶⟶ Trash Detection & Classification ⟶⟶ System Testing & Optimization ⟶⟶ Deployment & Documentation Key Highlights ✔ AI-powered marine trash detection — deep learning model trained to detect plastic, metal, rubber, fabric, fishing gear, wood, and more. ✔ CNN-based classification system — accurate differentiation between marine debris and natural underwater life. ✔ Advanced data preprocessing — automated cleaning, labeling, normalization, and augmentation to improve robustness. ✔ Underwater image optimization — enhanced performance under low visibility, turbidity, color distortion, and lighting variations. ✔ Real-time detection capability — works with underwater images and video streams for live monitoring. ✔ Actionable output — detected trash is highlighted with bounding boxes, labels, and location metadata for cleanup teams, AUVs, and ROVs. ✔ Scalable ML pipeline — modular training, evaluation, retraining, and deployment workflow. ✔ Model performance reporting — precision, recall, F1-score, confusion matrix, and training visualization. ✔ Clean & documented source code — reproducible training pipeline, deployment scripts, and full technical documentation. ✔ Research-ready delivery — suitable for academic, environmental, or commercial marine conservation use.

@huzaima372

Hi there, I am a Machine Learning Engineer specializing in computer vision and deep learning, with proven experience delivering real-world AI solutions. One of my key projects is the Sea Trash Detection and Cleanup System, an AI-powered solution designed to detect and classify marine debris such as plastic, metal, fabric, rubber, fishing gear, and wood from underwater images and video streams. The system uses a convolutional neural network (CNN) trained on labeled datasets containing both marine trash and marine life, enabling accurate differentiation under challenging underwater conditions such as low visibility, lighting variations, and water turbidity through automated preprocessing and data augmentation. Detected trash is highlighted with its location and category, allowing autonomous underwater vehicles (AUVs), remotely operated vehicles (ROVs), or human divers to perform targeted cleanup operations efficiently, reducing manual inspection efforts and supporting ocean conservation, environmental agencies, and sustainable marine ecosystem management. Workflow Diagram: Underwater Images / Video Streams ↓ Data Collection & Labeling ↓ Image Preprocessing & Augmentation ↓ CNN-Based Trash Detection & Classification ↓ Trash Type & Location Identification ↓ Actionable Output (Bounding Boxes + Categories) ↓ AUVs / ROVs / Human Divers ↓ Targeted Sea Cleanup Operations

@husseint0

I'm Hussein Tarek, a cybersecurity engineer and programmer skilled in securing systems, building apps, and AI automation for efficiency. My expertise in AI-driven solutions aligns with your marine debris detection project using deep learning and computer vision. Key Requirements Understood: CNN model for detecting/classifying trash (plastic, metal, etc.) vs. marine life from images/videos. Data preprocessing, augmentation for robustness (low visibility, turbidity). Integration with AUVs/ROVs for cleanup; support ocean conservation. My Approach: Develop CNN using Keras/Python (with Pandas for data handling, OpenCV for image processing). Train on labeled datasets with augmentation; implement detection logic for real-time highlighting. Ensure secure, efficient deployment; provide docs for maintenance. Experience: Built AI automation systems, including ML models for image analysis (e.g., object detection reducing manual effort by 70%). Proficient in Deep Learning, Computer Vision, Data Processing; secured AI apps handling sensitive data. Similar environmental AI projects delivered. Let's protect oceans! Available for discussion. Best, Hussein Tarek

@rakam26

Hello Just read your post and it seems you are looking for an AI Engineer skilled in computer vision, deep learning and underwater object detection for marine trash classification With my years of extensive experience and exceptional expertise n CNN-based image/video analysis, object detection, data augumentatioin, and building robust computer vision models for challenging environments, I am 100% confident that I can bring your vision to life in the shortest possible time. Let's connect adn see how great value I can add to your business. Best Regards Raka

@Artivisuallabs

Hi There, This seems an interesting project and I understood your product as well but have a lot of doubts considering Object recognition through underwater images and classifying patterns into Plastic/Metal waste by a learned system which is a never ending task. My approach would be to considering everything as garbage which is not an aquatic life or hydrophyte/macrophyte and then train the system on eliminating false positives to achieve better accuracy to start off with. I would love to discuss more around this if you are fine with talking and then estimating the efforts. Placing the bid as per my understanding as of now, can discuss and revisit the commercials and timelines. Thanks Anil Kumar

@vysakhm3

Hello, I’m interested in working on your Sea Trash Detection and Cleanup System and can support the project end-to-end using deep learning and computer vision techniques. I can develop and train a CNN-based model to accurately detect and classify marine debris such as plastic, metal, fabric, rubber, fishing gear, and wood from underwater images and video streams. I will handle data preprocessing, labeling support, and image augmentation to improve robustness under challenging underwater conditions like low visibility and lighting variations. The model output will include object location and class labels suitable for integration with AUVs, ROVs, or diver-assisted workflows. I will provide a trained model, evaluation metrics, inference scripts, and clear documentation for deployment and future improvements. Happy to discuss datasets, hardware constraints, and performance goals before starting.

@markdennis19

Hi, i am a machine learning engineer with full experience building object detect&tracking product. I am familiar to opencv, yolo, darknet, etc. I am confident that my skills are fit to this project. Thanks.

@mohymohamed004

As a Deep Learning and Computer Vision expert, I am highly interested in building your Sea Trash Detection system. Underwater environments present unique challenges like low visibility and light refraction; I specialize in image restoration and augmentation techniques (like Dehazing and Color Correction) to ensure high model robustness. I will implement a custom CNN/YOLO-based architecture to accurately classify debris from plastics to fishing gear while distinguishing them from marine life. My focus is on delivering an inference-optimized model ready for deployment on ROVs or AUVs. Let’s leverage AI to drive meaningful ocean conservation. I am ready to start immediately

@PremchandGat

Hello, I’m excited about this project because it combines deep learning, computer vision, and real-world environmental impact—areas I actively work in. I have hands-on experience building CNN-based classification systems, particularly for challenging visual conditions such as low light, noise, and occlusion. For your Sea Trash Detection and Cleanup System, I will develop a robust end-to-end pipeline that includes: Dataset curation, preprocessing, and underwater-specific augmentation (turbidity, color shift, low visibility) Training and fine-tuning CNN/object-detection models (e.g., YOLO/Faster R-CNN or custom CNNs) Accurate classification of debris types while differentiating marine life Real-time or near-real-time image/video inference with bounding boxes and class labels The system will be designed for integration with AUVs, ROVs, or diver-assisted workflows, ensuring practical deployment—not just academic results. I prioritize model accuracy, robustness, and interpretability, and provide clean, well-documented code with clear deployment guidance. I’m passionate about applying AI to ocean conservation, and I’d be happy to discuss your data, hardware constraints, and performance goals. Best regards

@kanisht9

Hello, I trust you're doing well. I am an ML engineer with strong expertise in computer vision, deep learning, and Python-based AI systems. My experience includes developing end-to-end vision pipelines for real-time object detection and classification—giving me the technical foundation to build robust sea trash detection systems for marine conservation. I've built production-grade computer vision solutions including an AI-powered CCTV system using YOLO and OpenCV for real-time vehicle and license plate detection with advanced image enhancement techniques for low-light conditions. I'm proficient in Python, TensorFlow, PyTorch, and hold Stanford certification in Machine Learning. My hands-on experience with CNNs, data preprocessing, image augmentation, and handling challenging visual conditions (low visibility, lighting variations) means I can develop accurate trash classification models that differentiate between marine debris and natural underwater elements. I understand the importance of building robust, scalable pipelines for real-world deployment on AUVs and ROVs. Your ocean conservation project piqued my interest, and I would be delighted to contribute to sustainable marine ecosystem protection through AI-powered solutions. I'm available 30-40 hours weekly and ready to start immediately. Let's connect to discuss in detail. Warm regards, Kanish Thakkar