Stop Sign Detection for Autonomous JetRacer

@ZohaibRoy

⭐⭐⭐⭐⭐ Real-Time Stop Sign Detection on JetRacer Using Jetson Nano ❇️ Hi My Friend, I hope you're doing well. I reviewed your project details and see you're looking for a skilled freelancer for stop sign detection on the Waveshare JetRacer. You don’t need to look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects for real-time object detection. I will develop a lightweight model using YOLO, optimize it for the Jetson Nano, and ensure it runs efficiently with low latency. ➡️ Why Me? I can easily handle your stop sign detection project as I have over 5 years of experience in computer vision and deep learning. My expertise includes Python programming, real-time object detection, and performance optimization. Additionally, I have a strong grip on embedded AI systems, ensuring a reliable solution for your JetRacer. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. Looking forward to discussing this with you! ➡️ Skills & Experience: ✅ Python Programming ✅ YOLO Object Detection ✅ TensorRT Optimization ✅ Real-Time Systems ✅ ROS Integration ✅ Computer Vision ✅ Deep Learning ✅ Embedded AI ✅ Model Training ✅ Performance Tuning ✅ Data Processing ✅ Camera Calibration Waiting for your response! Best Regards, Zohaib

@TheWorkingGroup

Hi! This is my area of interest and expertise! I develop custom YOLO models for custom object detection. If I could look into one of your videos containing the stop sign, that'd be nice. Looking forward to working with on this exciting project! Best, Salaar

@quan9824

https://www.freelancer.com/projects/raspberry-pi/Powered-Monitoring-Prototype-Development/reviews Hello, I’d be glad to support your JetRacer stop-sign detection project. I have strong experience with embedded AI, computer vision, and performance-focused deployment on resource-constrained systems, and this project is a very practical fit for that skill set. For your Waveshare JetRacer on Jetson Nano, I can help build a lightweight YOLO-based stop sign detector that runs fully onboard, optimize it for real-time inference, and integrate the detection-to-stop pipeline so the vehicle responds reliably with low latency. My approach would focus on: - lightweight detection model suitable for Nano - TensorRT optimization for faster inference - stable camera-to-inference pipeline - reliable stop trigger logic to reduce false positives/missed detections - real-hardware validation on JetRacer environment The goal is not just detection accuracy, but a working on-device system that behaves consistently in real driving conditions. I understand the importance of keeping everything onboard without relying on an external PC. I’d be happy to help you turn this into a reliable real-time JetRacer implementation. Best regards

@Ekarthaan

Hi Skander D., This is quite similar to a project I delivered last week, so I can jump straight into execution. Ready to start immediately. Which ROS stack should this integrate with (ROS1 Noetic or ROS2), and what existing topics/nodes currently control JetRacer throttle so we can hook a stop command cleanly? What camera is on the Nano (CSI IMX219 or USB), and what target resolution/FPS should be sustained end-to-end (e.g., 640x480@30)? Suggestion 1: Fine-tune a YOLOv8n/YOLOv5n model only for “stop sign,” then export to ONNX and build an INT8 TensorRT engine using calibration frames from your track; this typically doubles throughput on Nano vs FP32 and keeps accuracy on-domain. Suggestion 2: Add a low-cost temporal filter + geometric/color sanity checks (octagon aspect, red hue ratio) with a 2–3 frame persistence rule to slash false positives without extra heavy compute. Action Plan: - Env/I/O: set nvpmodel 10W + jetson_clocks; GStreamer nvarguscamerasrc zero-copy; measure baseline latency. - Data: capture onboard video, label stop signs, augment (lighting, motion blur). - Train: fine-tune YOLO (320–416 input), validate mAP and precision at desired range. - Optimize: export ONNX; build TensorRT FP16→INT8; prune if needed; profile to hit target FPS. - ROS node: camera ingest, TRT inference, temporal gating; publish /stop_sign and a latched stop flag; command JetRacer PWM to ramp throttle to zero and hold for T seconds Best Regards, Sid

@jushuamurillo98

Hello, I’ve carefully reviewed your project and am excited about the opportunity to work with you. With 6 years of experience in embedded AI and computer vision, I specialize in delivering optimized real time detection systems for edge devices like the Jetson Nano. I am confident I can build an efficient stop sign detection pipeline that runs fully on your Waveshare JetRacer with low latency and reliable stop triggering. Here’s my approach: Develop and train a lightweight YOLO model tailored for stop sign detection, optimized for Nano. Convert and accelerate the model using TensorRT and integrate it into a ROS based pipeline for real time control. I am available to start immediately and aim to deliver a fully integrated working solution by the end of the week. Additional instructions / notes (optional): I will test and validate performance directly on Jetson hardware. I will ensure stable inference speed and smooth ROS integration. Best regards, Jushua

@maximg10

Hello. Thanks for your job posting. ⭐Stop Sign Detection for Autonomous JetRacer⭐ I'm the developer you're looking for. I can successfully complete your project. Let's chat for a more detailed discussion. Thank you. Maxim

@Sohail748

Hii there, I’m offering a 30 percent discount for this project and would be glad to assist you in developing stop sign detection for your autonomous JetRacer. With experience in computer vision, deep learning, and embedded AI systems, I can build a reliable detection pipeline tailored for real-time performance on edge devices. My approach will focus on implementing an efficient object detection model (such as YOLO or a lightweight CNN) trained to accurately recognize stop signs under different lighting and environmental conditions. I will optimize the model for JetRacer hardware, ensuring low latency and smooth integration with the vehicle’s control system to trigger appropriate stop behavior. I can also handle dataset preparation, model training, validation, and deployment, along with testing in real-world scenarios to ensure accuracy and stability. The final solution will be scalable and adaptable for detecting additional traffic signs if needed. As a dedicated freelancer, I prioritize attention to detail, clear communication, and delivering robust, production-ready AI solutions. I am confident that I can implement an effective stop sign detection system for your JetRacer. Kind regards, Sohail Jamil

@markoa7

With over a decade of experience in electronic hardware and firmware engineering, including extensive experience with embedded systems and various microcontrollers, I believe I'm highly equipped to accomplish the task of stop sign detection for your JetRacer. My competency in PCB design and development lends itself well to optimizing the hardware for your application, ensuring that the solution runs smoothly and efficiently on the Jetson Nano board. In addition to my firmware skills, I'm also proficient in Python, which is key to developing and training a lightweight object detection model. Coupled with my strong background in real-time object detection, I have the capabilities to build an effective YOLO model tailored specifically for your JetRacer. My expertise extends to wireless technologies like Wi-Fi and Bluetooth - invaluable for integrating your solution into a ROS-based pipeline if desired. Your vision becomes my drive, and committing to your project means delivering not only on time but at an unmatched quality. We can certainly discuss more about how I can contribute to making your autonomous driving project safer and more efficient.

@softsolution2000

I have extensive experience in Python, C++ Programming, Arduino, Deep Learning, and Object Detection, making me a great fit for the "Stop Sign Detection for Autonomous JetRacer" project. The budget can be adjusted after discussing the full scope, and I am committed to working within your budget. I am confident and eager to start working on this exciting project. Please review my 15-year-old profile to see my past work. Let's discuss the job details and get started right away.

@abhisaini0188

As an 8+ years experienced developer, I've specialized in creating AI-powered applications with a strong focus on Machine Learning integration. Previously I have worked with tasks close to the one you're offering; from building and training lightweight object detection models (such as YOLO) to optimizing them for efficient realtime performance, using frameworks like TensorRT and even while conducting these operations directly on the embedded platforms like Jetson Nano. Throughout my career, I've helped businesses build intelligent systems that automate their workflows which aligns perfectly with your project. I can deliver the practical and reliable stop sign detection solution you need by combining my skills in Computer Vision and Deep Learning with a strong technical background in Real-time object detection. You won't have to worry about stability or latency issues; maintaining real-time behavior from the system is one of my top priorities. By choosing me, you'll be getting more than just a freelancer- you get a collaborative partner that doesn't stop at coding but thinks strategically about your business impact. I'd love to ensure your project not only meets expectations but exceeds them, so let's get started on building a smart and impactful system for your JetRacer together.

@dvcontact

Hello Sir, Would you like me to build a demo of the stop sign detection system on the JetRacer before any commitment? I specialize in developing optimized object detection models and have extensive experience with Jetson Nano, ensuring a real-time, reliable solution tailored to your project needs. Let's discuss this further to create a detailed plan and demo that showcases the capabilities of the system on your JetRacer. Regards, Smith

@hayat38402

Hi, how are you doing? I went through your project description and I can help you in your project. your project requirements perfectly match my expertise. We are a team of expert engineers, we have successfully completed 1000+ Projects for multiple regular clients from OMAN, UK, USA, Australia, Canada, France, Germany, Lebanon and many other countries. We are providing our services in following areas:  Neural Network/ Natural Language Processing  Machine learning/Data Mining  Deep Learning and Computer Vision  Image Recognition & Artificial Intelligence  AI text analysis model and Reinforcement Learning.  Omnet++ and Sumo simulation,  Python/ MATLAB  Asterisks PBX  NS3 simulation  Linux We'll make sure that your project is done in a perfect way and do our best until you were satisfied. I am confident I can provide you with top-notch materials that will fit your needs.

@hotanloc

Hello, I have already completed a similar project successfully. Project review: https://www.freelancer.com/projects/python/people-detection-counting/reviews Please take a look at my previous Freelancer projects and reviews. Your project aligns well with my experience, and I will do my best to meet all your requirements. My core expertise is in object detection, tracking, and counting. I have developed many computer vision projects, including: * People detection and counting * Product detection and counting * Defect detection in manufacturing * Vehicle detection and speed analysis I have strong experience in image processing and CCTV video analysis, where objects are detected and counted from images or video streams. For your project, I will: 1. Train a model using annotated data to generate optimized weights 2. Develop the detection system based on the trained model 3. Analyze detected objects and display the results clearly My technical stack includes YOLO, OpenCV, TensorFlow, PyTorch, Keras, OCR, and other ML/DL frameworks. With my experience in machine learning and deep learning, I can build an accurate detection system and implement post-processing (such as object counting and analysis) using OpenCV. I am confident I can deliver a high-quality solution within a short timeframe. Please feel free to send me a message so we can discuss your project in more detail. I look forward to hearing from you. Thank you.

@alizain721

Getting a JetRacer on a Jetson Nano to reliably see a stop sign and actually stop in real traffic-like conditions is harder than the tutorials make it sound. I enjoy that challenge and have tuned models specifically for small embedded racers. The best thing about me is I’ve worked on a very similar project recently. I trained a YOLOv5-tiny style model, converted it to ONNX and TensorRT, and integrated it as a ROS node on a Jetson Nano that reads the camera, runs optimized inference at >15 FPS, and publishes a stop command to the controller. I understand the flow: camera capture → preprocessing → lightweight detector → decision logic → motor stop command. System-wise I’ll use PyTorch→ONNX→TensorRT, a Python ROS node for inference, and a separate control node to ensure low-latency, safe stopping. I can reuse conversion scripts and ROS wrappers to save weeks of work. Do you have a preferred stop behavior (immediate cut vs controlled deceleration) and is there an existing ROS topic/motor interface on your JetRacer? Happy to hop on a quick call to align scope and timelines. My bid: 140 EUR. Regards Ali Zain!!

@asharib17

I can implement a reliable, real-time stop sign detection system directly on your Waveshare JetRacer (Jetson Nano), optimized for embedded performance. The approach will focus on a lightweight YOLO model (e.g., YOLOv5n/YOLOv8n) trained specifically for stop sign detection, then optimized using TensorRT to achieve low-latency inference suitable for onboard execution. I’ll ensure the full pipeline—from camera input to detection to stop action—is stable and efficient. Core implementation: • Train/finetune lightweight YOLO model for stop sign detection • Convert and optimize model with TensorRT for Jetson Nano • Integrate real-time camera feed processing (CSI/USB camera) • Implement detection-to-action logic (trigger stop reliably) • Optional ROS integration (node for detection + control messaging) Deliverables: • Trained and optimized model (TensorRT engine) • End-to-end pipeline running on Jetson Nano (no external compute) • Real-time detection with low latency and stable FPS • Clean Python code with clear structure and comments • Setup + deployment guide (Jetson environment, dependencies, run steps) I’ve worked on embedded AI and real-time vision pipelines, focusing on optimizing models for constrained hardware like Jetson devices.

@nemanjac6

Hello there! From your description, this involves a real‑time detection workflow on the Jetson Nano, and I’d approach it by shaping a lightweight inference pipeline optimized through TensorRT and integrated cleanly into ROS. A key constraint here will be keeping latency low while maintaining detection accuracy on Nano’s limited GPU. I’ve built embedded ML pipelines on Jetson boards before, including TensorRT‑optimized YOLO variants running on camera feeds. A practical way to handle this is: - Build a Nano‑friendly YOLO model, prune/quantize, and convert it into a TensorRT engine. - Design a Python-based inference module managing frame capture, preprocessing, and stable detection thresholds. - Integrate the detection node into ROS so the stop action is triggered reliably with minimal processing delay. One insight: Nano benefits massively from pre‑allocated GPU buffers and fixed‑size tensors to avoid runtime overhead. Do you already have a preferred YOLO variant (YOLOv5n, YOLOv7‑tiny, YOLO‑NAS, etc.) for the Jetson Nano performance target? Happy to take a closer look if needed. , Nemanja

@huonghy89

Hi, How are you? Very happy to bid for your project because my skills are fitted in your project. I am 8 years of experience in Machine learning, deep learning, OCR, Image processing and computer vision. I am very familiar with openCV, MTCNN & Facenet, YOLO, GPU, Mediapipe, Generative Adersarial Network, openAI, CNN, RNN, GAN, LSTM, SVM, reinforement learning, opencv, Pytorch, tensorflow, keras, tesarret Pandas, sklearn, numpy, matplotlib, seaborn and so on. I have made the apps for ANPR/ALPR, cancer detection, image classification and recognition, object detection and tracking, sentiment classification, license recognition, ID card recognition, hand gesture recognition, face emotion recognition, face swapping, virtual cloth trying, template matching. I can do your project perfectly. If you send the message , we can discuss the project more. Thanks.

@kend27

Hi, I am a Senior AI/ML Engineer with hands-on experience in computer vision, edge AI deployment, and real-time object detection. For this project I would train a lightweight YOLOv8n model on stop sign data, optimize it with TensorRT for Jetson Nano, and wrap the detection pipeline in a ROS node that triggers a stop command on detection. The entire system runs onboard with low latency. Deliverables include the TensorRT-optimized model, ROS integration, setup documentation, and a demo video showing real-time detection on the JetRacer. Timeline: 2 to 3 weeks depending on dataset availability. Happy to discuss further.

@krisk63

Hi there, I'm Kristopher Kramer from McKinney, Texas. I’ve worked on similar projects before, and as a senior full-stack and AI engineer, I have the proven experience needed to deliver this successfully, so I have strong experience in Arduino, Python, YOLO, C++ Programming, Object Detection and Deep Learning. I’m available to start right away and happy to discuss the project details anytime. Looking forward to speaking with you soon. Best regards, Kristopher Kramer