ROS2 Nut-Bolt Vision Detection

@avinashdubeyg209

Hi there, I am an ML engineer. I can start right away and deliver within the deadline. So, Let’s have a quick conversation. I can be more specific once we get all the requirements and information required to execute the project. Thank you!!

@FaizalShaik80

Hi, I would love to help. I have reviewed your project and noticed that it is very similar to a task I completed two months ago. I am an experienced and specialized freelancer with 6+ years of practical experience in C Programming, Python, C++ Programming and I’m able to complete and deliver this project promptly. Feel free to visit my profile to check latest work and feedback from clients. Looking forward to working with you, connect in chat. Talk soon.

@ivaylo8

Hello, I can help build a reliable ROS 2 vision detection module that identifies nuts and bolts in a live camera feed and publishes their positions in real time. My approach would be to fine-tune a lightweight object detection model (such as YOLO or a PyTorch-based detector) optimized for real-time performance. The model will be integrated into a ROS 2 Humble node that subscribes to the camera image topic, performs inference, and publishes detected objects with bounding boxes, class labels, and confidence scores. The pipeline will be designed to run GPU-agnostic, while allowing optional CUDA acceleration if available. Deliverables will include: • Complete source code for the trained detection model and ROS 2 node • A launch file to start the detector with default parameters • A clear README covering setup, dependencies, parameters, and ROS topics • A demo video or ROS bag showing successful detection on your sample parts For training, approximately 300–800 labeled images should be sufficient depending on variation, and I can help with dataset preparation if needed. The system will be optimized to meet your ≥90% precision/recall target and 10+ FPS at 640×480. Estimated timeline: 1–2 weeks including training, integration, and testing. I’d be happy to review your sample images and camera setup to begin. Best regards

@GameOfWords

ROS2 Nut-Bolt Vision Detection I’m a full-stack software engineer with expertise in React, Node.js, Python, and cloud architectures, delivering scalable web and mobile applications that are secure, performant, and visually refined. I also specialize in AI integrations, chatbots, and workflow automations using OpenAI, LangChain, Pinecone, n8n, and Zapier, helping businesses build intelligent, future-ready solutions. I focus on creating clean, maintainable code that bridges backend logic with elegant frontend experiences. I’d love to help bring your project to life with a solution that works beautifully and thinks smartly. To review my samples and achievements, please visit:https://www.freelancer.com/u/GameOfWords Let’s bring your vision to life—connect with me today, and I’ll deliver a solution that works flawlessly and exceeds expectations.

@Mansoor1290

Hello, I’d be happy to help you build the ROS 2 vision-detection module for identifying nuts and bolts in a live camera stream. I have experience working with **ROS 2 (Humble), OpenCV, and deep-learning based object detection pipelines** using **PyTorch/TensorFlow**, and integrating them into real-time robotic systems. For this project, I propose building a lightweight detection pipeline using **YOLO-based architecture (PyTorch)** because it provides strong accuracy with real-time performance and is easy to integrate with ROS 2 nodes. My approach will be: • **Model Training / Fine-Tuning:** I will fine-tune a pretrained detector on your nuts and bolts dataset to reach the required ≥90% precision/recall. • **ROS 2 Integration:** I will implement a **ROS 2 Humble node** that: * Subscribes to the camera image topic (via `image_transport`) * Runs inference in real time * Publishes detected objects with **bounding boxes, labels, and confidence scores** * Supports optional CUDA acceleration but remains **GPU-agnostic** • **Performance Target:** The node will be optimized to achieve **10+ FPS at 640×480** on a standard CPU system. Deliverables will include: • Full **source code** for the trained model and ROS 2 detection node • **Launch file** with configurable parameters (camera topic, confidence threshold, etc.) • **README documentation** explaining setup, dependencies, and ROS topics

@devbaloni1983

Hello, I am a Python Developer with 15+ years of experience in building secure, scalable, and high-performance applications. I specialize in Python-based backend development, automation scripts, API development, data processing, and integrating third-party services. My expertise includes Django, Flask, Fast-API, REST APIs, MySQL/PostgreSQL, and cloud deployment. I also recently worked on integrating the OpenAI API for auto-generated content, images, and automation features—showing my ability to adopt modern AI technologies. If you are looking for a dedicated Python Developer who delivers clean code, reliability, and fast results, I’d be glad to work on your project.

@antonk52

Greetings! I see you're working on a ROS 2 pipeline to detect nuts and bolts from a live camera feed. My approach would involve training a robust detection model using TensorFlow or PyTorch, depending on your preferences, and then creating a ROS 2 node that can efficiently process the images and publish the coordinates of the detected objects with bounding boxes and confidence scores. With experience in C++, Python, and computer vision tools like OpenCV, I can ensure that the final implementation meets your requirements for precision, recall, and throughput. I’d be happy to utilize your test rig and any calibration images you can provide to fine-tune the model for optimal performance. Once everything is set up, I’ll deliver the source code, a launch file, and a README to help you get started smoothly. Best regards,

@AnjosGustavo

Hello, I have already experienced that kind of projects, developing real-time vision modules to detect small industrial parts (nuts, bolts) and publishing their poses into ROS 2 pipelines without relying on specific GPU hardware. The main challenge is achieving robust detection under variable lighting, partial occlusion and ensuring >=90% precision/recall while keeping throughput ≥10 fps at 640×480. I'll solve this by fine-tuning a lightweight detection model (e.g., EfficientDet-lite or YOLOv5/YOLOv8-small) with targeted augmentations and ROI-aware preprocessing, implementing a ROS 2 Humble node (Python/C++) that subscribes to image topics via image_transport, outputs bounding boxes/masks and confidence, and includes optional CUDA hooks. I will supply model training scripts, the node, a launch file, README and a demo bag/video. Looking forward to working with you. Best regards, Gustavo.

@josec298

Hello, With over 7 years of experience as a Full Stack Developer, my work has spanned across different industries from e-commerce to AI/ML integrations and mobile app development. Your project of building a ROS2 pipeline to detect nuts and bolts fits right into my wheelhouse. While I have not worked directly on vision detection using OpenCV or TensorFlow/PyTorch, I have the necessary skillset in Python and a knack for learning new tech quickly. Within your working parameters, I aim not just to meet but to surpass your expectations. Having delivered 180+ successful projects with a proven 100% client satisfaction rate, quality is my utmost priority. And with such a rigorous acceptance criterion, you can trust that I'll leave no stone unturned to achieve the 90% precision/recall and 10 fps throughput at 640×480 that you're looking for. Lastly, my dedication to delivering clean code with thorough documentation alongside excellent problem-solving skills makes me an ideal candidate for this project. You can expect timely communication and full support even after the project completion because ultimately it's not just about delivering the project but also ensuring its smooth functioning in future scenarios. Let's get started on creating the complete vision-detection module for your ROS2 pipeline! Thanks!

@fighter6869

Hello, With over 10 years of experience in C++, Python, and OpenCV, I have the expertise required to tackle your ROS2 Nut-Bolt Vision Detection project. I understand the need for a reliable vision-detection module that can accurately spot nuts and bolts in real-time camera feeds within a ROS2 pipeline. I specialize in developing solutions that automate workflows and enhance e-commerce performance. My skills cover frontend development using React and backend development with Node.js and Python. Additionally, I have experience in database management, AI integration, and e-commerce platforms like Shopify and WooCommerce. I am eager to discuss your project further and collaborate on creating a robust vision-detection module. Looking forward to connecting with you in chat. Thanks.

@ivicab6

Hello, With over 6 years of experience in Python, I specialize in computer vision and machine learning projects. I have a strong background in creating ROS 2 pipelines and developing vision-detection modules using OpenCV and TensorFlow. I have thoroughly reviewed your project requirements for the ROS2 Nut-Bolt Vision Detection. I am confident in my ability to deliver a robust solution that meets your specifications. I will develop a custom vision-detection model, integrate it into a ROS 2 node, and ensure real-time object detection with bounding boxes and confidence scores. Having hands-on experience in similar projects, I am eager to take on this challenge and provide you with a high-quality deliverable. Effective communication is key, and I will ensure regular updates throughout the project. I am excited about the opportunity to work on this project and look forward to hearing from you. Best regards, Ivica

@Hemantt841

You’re looking to build a ROS 2 vision detection module that identifies nuts and bolts in a live camera feed, publishing their positions with bounding boxes or masks and confidence scores. Your requirement for a clean ROS 2 node compatible with Humble, GPU-agnostic but optionally CUDA-accelerated, and achieving at least 90% precision/recall at 10 fps on 640×480 images is clear and well-defined. With over 15 years of experience and more than 200 projects completed, I specialize in Python, C++, OpenCV, and deep learning frameworks like TensorFlow and PyTorch, all within ROS environments. I have hands-on experience developing real-time computer vision pipelines that integrate seamlessly with ROS 2 image-transport plugins and deliver reliable detection performance on embedded and desktop systems. I will start by assessing your validation dataset to determine the number of training images needed and select a suitable model architecture for fine-tuning or training from scratch. The ROS 2 node will subscribe to your USB camera topic, perform inference, and publish detection results with bounding boxes and confidence scores. I’ll provide all source code, a launch file, README, and demonstration video or bag file within roughly two weeks, ensuring your throughput and accuracy targets are met. Let’s discuss your dataset and timeline details so I can tailor the solution precisely to your test rig and validation criteria.

@natanr28

HELLO, HOPE YOU ARE DOING WELL! You need a complete vision-detection module for reliably spotting nuts and bolts in a live camera feed. My expertise aligns perfectly with your project goals. My plan involves training or fine-tuning a robust model using OpenCV with TensorFlow or PyTorch, followed by creating a clean ROS 2 node that subscribes to your image topic and outputs detected objects with bounding boxes or masks along with their confidence scores. I will ensure the final node runs on Humble and maintains GPU-agnostic capabilities. I'll also provide the complete source code, a launch file, and comprehensive README documentation. I'd like to have a chat with you at least so I can demonstrate my abilities and prove that I'm the best fit for this project. Warm regards, Natan.

@southa5

I am a PhD researcher in AI. I build computer vision pipelines in Python/TensorFlow/PyTorch with OpenCV, and integrating them into ROS2 as publishable nodes is straightforward for me. I'll train or fine-tune a detection model on your sample parts images to hit ≥90% precision/recall, wrap it in a clean ROS2 node that subscribes to your camera topic and publishes bounding boxes, masks, and confidence scores at ≥10 fps on your hardware. Delivery includes the model, launch file with default parameters, and a short demo video. Framework will be GPU-agnostic; CUDA acceleration added if needed. I'll need your sample images count to estimate training time — please share those and I can give you a precise timeline immediately.

@shabahathabib

Hello, Your requirement is a reliable ROS 2 Humble vision module that detects nuts and bolts from a live camera feed and publishes their positions in real time. I can build this using PyTorch with a lightweight object detection model (such as YOLO based architecture) integrated into a ROS 2 node that subscribes to the camera image topic and publishes bounding boxes with confidence scores. The node will use standard ROS 2 image transport and remain GPU agnostic while supporting optional CUDA acceleration if available. I will provide the trained model, ROS 2 node source code, launch file, and a clear README explaining setup, parameters, and expected topics. To achieve the target accuracy, I typically recommend around 400–800 labeled images covering different angles and lighting conditions. A working prototype can be delivered within a few days, followed by tuning to reach the 90% precision and 10 FPS requirement. Best Regards, Shabahat Habib.

@jeanpatrickr8

Hello, I appreciate the opportunity to work on your ROS 2 pipeline project focused on detecting nuts and bolts in real-time camera feeds. I understand that you require a complete vision-detection module that efficiently identifies these objects and publishes their positions accurately. With extensive experience in computer vision and robotics, I have successfully developed similar detection systems using OpenCV and TensorFlow. My expertise includes model training, fine-tuning, and creating robust ROS 2 nodes, ensuring compatibility with the latest versions and maintaining GPU-agnostic performance. To achieve your project goals, I propose the following approach: - Collaborate with you to determine the necessary training images for optimal model performance. - Develop and fine-tune the detection model, ensuring it meets the precision/recall requirements. - Create a clean ROS 2 node that subscribes to the image topic, outputs bounding boxes/masks, and includes a confidence score. - Provide comprehensive documentation and a launch file for easy setup and testing. I'm eager to start this project and am confident in delivering high-quality results that meet your specifications and deadlines. Please let me know if you’d like to discuss further details or clarify any requirements.

@jonhw5

Hello, thanks for posting this project. I will design a robust ROS 2 Humble module to detect nuts and bolts in live camera feeds, publish precise 3D-like positions, and keep the pipeline GPU-agnostic. The solution will cover data collection/annotation guidance, model training or fine-tuning (PyTorch preferred) to achieve 90%+ precision/recall and at least 10 FPS at 640x480, with a clean ROS 2 node subscribing to an image topic and publishing detections with bounding boxes (or masks) and confidence. Deliverables include: a trained detection model script, a C++/Python ROS 2 node for image subscription, a launch file with default parameters, a detailed README (setup, parameters, topics), and a small video or bag demonstrating detector outputs. The system will be modular for easy swapping between TensorFlow and PyTorch backends and will include GPU-agnostic code paths. I estimate needing a few hundred to a few thousand labeled images for robust performance, depending on the chosen model, and I will propose a concrete plan after alignment on data availability. What is your preferred training data split and calibration approach for the camera rig, and are there any specific ROS 2 topic conventions or coordinate frames you want used for the published detections? Looking forward to hearing from you. Best regards,

@jeremiasg17

Hi there, I can create a robust vision-detection module for your ROS 2 pipeline that reliably identifies nuts and bolts in real-time. With over 10 years of experience building similar systems, I’m confident in delivering a solution that meets your precision and throughput requirements. I’ll develop the detection model, ROS 2 node, and provide the necessary launch file and README for setup. I’m also open to starting with a small milestone to ensure we’re aligned from the get-go. Feel free to ask any technical questions you might have; I’m here to help. I value this collaboration and will treat it with the seriousness it deserves. Looking forward to discussing your project further!

@dragomirm1

Hello, With 10+ years of experience as a Full-Stack & AI Engineer, I bring to the table an impressive array of skills that are perfectly suited for your ROS2 Nut-Bolt Vision Detection project. Over the course of my career, I have developed a sharp acumen for turning complex ideas into secure and high-performance applications. And I now wish to channel that expertise into training or fine-tuning a vision-detection model that will cater exclusively to your needs. My competence in Python, OpenCV, TensorFlow/PyTorch, and ROS2 complements the tasks you have set out remarkably well. These competences have been honed over the course of more than 50 projects across different domains - from creating biometric systems and chatbots to delivering scalable SaaS platforms. This translates to a broad understanding of different nuances in AI development and how they can be efficiently leveraged. In terms of delivery, you can count on me to provide source code for the detection model and the ROS2 node along with a detailed README file that outlines setup instructions and expected topics. Additionally, you will also receive a launch file configured with default parameters - all aimed at ensuring ease-of-use on your end. Lastly, a video or bag file demonstrating the detector's successful operation on your sample parts will further reinforce my commitment to quality solutions. Knowing that our mutual goal is a 90% precision/recall rate on your validation set indefinitely Thanks!