Real Time Dart Detection System

@ZohaibRoy

⭐⭐⭐⭐⭐ Create an Automated Scoring System for Your Dartboard ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see you're looking for an automated scoring system for your dartboard. You don't need to look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects focused on real-time scoring systems. I will use Python/OpenCV or C++/OpenCV to ensure millisecond-level detection with high accuracy. ➡️ Why Me? I can easily do your automated scoring project as I have 5 years of experience in computer vision and real-time processing. My skills include webcam integration, image processing, and JSON event handling. Additionally, I have a strong grip on calibration techniques and backend service management, ensuring a seamless experience for users. ➡️ Let's have a quick chat to discuss your project in detail. I can share samples of my previous work that demonstrate my expertise in creating efficient scoring systems. Looking forward to our conversation! ➡️ Skills & Experience: ✅ Python Programming ✅ C++ Programming ✅ OpenCV ✅ Webcam Integration ✅ Real-Time Processing ✅ JSON Event Handling ✅ Calibration Techniques ✅ Image Processing ✅ API Development ✅ Data Analysis ✅ Performance Optimization ✅ Testing and Debugging Waiting for your response! Best Regards, Zohaib

@efanntyo

Hi there, I’m excited about building a real-time, headless Dart Detection System that combines three synchronized 1080p webcams with millisecond-level detection, robust calibration, and a scalable WebSocket-based event stream. As a full-stack developer and AI specialist, I bring hands-on experience in OpenCV-backed computer vision, multi-camera geometry, and low-latency networked apps, with a proven track record delivering production-ready CV pipelines and real-time data services across platforms. My approach is pragmatic, modular, and designed to be maintainable under pub-lighting conditions in a bar or pub setting. What I bring to your project - Real-time multi-camera fusion: I’ll implement a tightly-timed synchronization strategy across three USB webcams, leveraging timestamped frames and geometric calibration to map each detected dart to its accurate board segment. I’ll design a robust tri-camera calibration workflow that avoids chessboard targets and uses color/edge cues plus minimal motion-based calibration so a pub user can run it in ~2 minutes. - Accurate segmentation and scoring: Using OpenCV with either Python or C++, I’ll implement fast edge-aware segmentation, contour-based dart tip localization, and a model-free hit-detection routine that maps hits to board rings and segments with ≥98% accuracy. I’ll account for lighting variance with adaptive exposure/gain control and dark-frame subtraction, ensuring stable performance across different pub conditions. - Real-t

@intelliresponse

Hi there. As a highly specialized Python developer with extensive experience in AI, I'm confident that I possess the right skills and mindset to take on your Real-Time Dart Detection System project. Having worked on numerous computer vision projects involving OpenCV, I'm well-acquainted with the requirements of acheiving millisecond-level detection with utmost precision. My profound understanding of multi-camera geometry and troubleshooting lighting variances equip me to handle any challenges which may arise and ensure consistent accuracy and real-time event stream delivery. Lastly, my commitment towards high-quality performance is evident not only in exceeding client expectations but also documentation which will be transparently presented in your performance report. All in all, choosing me for your Dart Detection project means engaging a seasoned professional who will deliver exemplary accuracy, satisfying latency levels, quick calibration process and comprehensive documentations without compromising quality or timeline. Let's connect soon to delve deeper into your vision for this project and discuss how my skills can best align with your needs.

@elhadfi

Hey there, This is exactly the kind of real-time CV challenge I love — I’m a software & AI engineer with deep OpenCV + multi-camera geometry experience, and I’ve built low-latency vision systems before. I’d approach this with calibrated homography per camera + triangulation for impact localization, optimized frame differencing, and a lightweight WebSocket JSON emitter — easily within ≤150ms on a mid-range PC. With my experience, I’m sure I can finish this task in a very short time, assuring the expected results. I’m a Verified & Preferred Freelancer (5.0⭐, 100% on-time, 100% on-budget, 52+ happy clients). Feel free to check my profile and contact me for more details. Quick question — do you already have fixed camera positions, or should I design the geometry to tolerate slight pub-level mounting variations?

@ambar

Are cameras fixed once mounted or do they shift slightly between sessions? Multiple darts on board at same time or always one throw detect? Is audio spike allowed as secondary trigger? I went through your description carefully. I’ve done similar automation work before. I’d go C++ + OpenCV here. Python is ok but 150ms target is tight and C++ gives better frame control. Each cam runs its own capture thread (separate buffers, no blocking). Impact = frame delta + motion spike + board mask filter. Then cluster dart tip using contour + RANSAC, not just centroid. Small repeat here because centroid alone is risky. Calibration won’t need chessboard. Detect outer double ring via circular Hough, align 20 at top using angle sweep, quick 2–3 click correction if needed. Save extrinsics to local JSON so pub staff don’t recalibrate again and again. Other decisions I’d include: – temporal median filter (3 frames) to reduce flicker – polar precomputed segment map (angle + radius bins cached in memory) – confidence threshold before emitting event – async non blocking WebSocket queue – micro latency logs per stage – frame ring buffer for replay tests – radial ratios stored normalized to board size Events pushed instantly after triangulation. Headless service only. Simple WS test client included. Prefer full 3-camera triangulation or 2-camera solve + 1 for validation only?

@corpmember29

{{{ I HAVE CREATED SIMILAR APPS BEFORE AND I CAN SHOW YOU }}} I can build a headless, real-time dart scoring service using multi-camera computer vision with a strong focus on latency and accuracy. Approach 3× synchronized 1080p webcams with timestamp alignment ython + OpenCV (C++ optional if profiling shows gains needed) Impact detection via frame differencing + temporal filtering Multi-camera triangulation to get precise (x, y) board coordinates Fast polar mapping to ring/segment → score resolution Calibration 2-minute guided routine using the dartboard itself (no chessboards) Automatic board center, radius, and camera pose estimation Robust to pub lighting and shadows Output Headless service WebSocket JSON events on confirmed impact Example: {x, y, ring, number, score} Deliverables Source code + build/run docs Simple calibration workflow + printable aids (if needed) WebSocket API spec + minimal test client Performance report with latency, accuracy, and test footage Targets ≥98% segment accuracy ≤150 ms end-to-end latency on mid-range PC Happy to walk you through geometry, lighting handling, and optimization strategy.

@maximg10

Hello. Thanks for your job posting. ⭐Real Time Dart Detection System⭐ 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

@ducthodg

As an industrial engineer with over a decade of experience in building complex algorithmic systems, I believe I possess the unique combination of skills necessary to tackle this fascinating and specialized project. While my expertise primarily lies in financial software development, I am well-versed in languages like C++, C, and Python; all of which would seamlessly fit your tech stack needs for the dart detection system. Furthermore, my well-rounded programming background will ensure that the system can not only detect darts with √≥98 % accuracy but also provide real-time event streaming, adhering to your JSON format. Lastly, with my extensive programming portfolio and deep understanding of system architecture combined with hardcore python’s OpenCV library, I know how to reach millisecond-level detection with reliable precision. A detailed performance report on average latency, detection accuracy and test footage will be shared as per the deliverables list: granting you complete transparency and confidence! If you seek not just a coder, but a diligent craftsman ensuring smooth darts flying and scores flowing, let's collaborate!

@quan9824

&& YOLO, OCR, OpenCV, Tensorflow, PyTorch, Keras, ML/DL model && Hi, How are you?. I have full skills and full experiences of this field. I have developed many Image Processing project and I am expert in these fields I can finish your project with high quality and on time. Please send me your message to discuss more about your project. I am waiting your reply now. Thanks.

@juanjosec63

Hello, I’m excited about the opportunity to contribute to your project. With my expertise in Python/C++ OpenCV computer vision, multi-camera synchronization and geometry (homography/triangulation), fast impact detection and segmentation, robust low-light/lighting-variance handling, and low-latency WebSocket JSON event streaming with a minimal test client and performance benchmarking, I can deliver a solution that aligns perfectly with your goals. I’ll tailor the work to your exact requirements, ensuring smooth integration, reliable performance, and a refined user experience. You can expect clear communication, fast turnaround, and a high-quality result that fits seamlessly into your existing workflow. Best regards, Juan

@abhisaini0188

Hello, I am really excited about the opportunity to collaborate with you on this project! It aligns perfectly with my skill set and experience, and I’m confident I can contribute meaningfully to your vision. I genuinely enjoy working on projects like this, and I believe we can create something both functional and visually engaging. Please feel free to check out my profile to learn more about my past work and client feedback. I’d love to connect and discuss the project details further your goals, expectations, and any specific features or ideas you have in mind. The more I understand your vision, the better I can bring it to life. I am ready to get started right away and will put my full energy and focus into delivering quality results on time. My goal is not just to complete the project, but to exceed your expectations and build a long-term working relationship. Looking forward to hearing from you soon! With regards! Abhi

@GameOfWords

Real Time Dart Detection System 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.

@hotanloc

Hi,Thanks for your posting. I have full skills and full experiences of this field. I have developed many Image Processing project with every field of image processing. I am expert in these fields (YOLO, OCR, OpenCV, Tensorflow, PyTorch, Keras, ML/DL model). I can finish your project with high quality and on time. Please send me your message to discuss more about your project. I am waiting your reply now. Thanks.

@sekai1116

I can develop a real-time dart detection system that meets your requirements. Using Python with OpenCV, I would process the three synchronized webcam feeds, detect dart impacts, and map them to board segments via a multi-camera triangulation approach. The system will include an easy calibration routine that anyone can run in under 2 minutes—no specialized targets or lab lighting required—and automatically adjust for minor lighting variations or camera offsets. Once a dart is detected, the system will immediately compute the segment, ring, and score, then push the data as a JSON event over WebSocket for instant updates to any client. I will deliver fully commented source code, a calibration workflow, and a minimal test client that demonstrates real-time event streaming. Additionally, I will provide a performance report including detection accuracy and latency tests to ensure ≥98% hit-segment accuracy and end-to-end latency under 150 ms on a standard mid-range PC. This approach ensures a robust, pub-friendly setup that reliably tracks scores in real time.

@mobiwebsolutions

✋ Hi There!!! ✋ The Goal of the project:- To build a high accuracy real time multi camera dart detection service that identifies segment hits and emits instant JSON scoring events via WebSocket. I carefully reviewed your requirement for three synchronized 1080p webcams, millisecond level detection using Python or C++ with OpenCV, fast pub friendly calibration without complex targets, and a headless service delivering structured JSON events with under 150 ms latency and at least 98 percent accuracy. I understand the importance of multi camera geometry, lighting robustness, and performance validation. With 9+ years experience as a full stack developer and computer vision engineer, I specialize in real time image processing systems. • Multi camera calibration and triangulation for precise hit localization • Optimized OpenCV pipeline for sub 150 ms detection latency • WebSocket API emitting structured JSON score events I will provide testing framework, performance benchmarking, API documentation, database logging if required, and full source code delivery. I have built similar vision based tracking and scoring systems using OpenCV and real time streaming. Looking forward to chat with you for make a deal Best Regards Elisha Mariam!

@Azursol

Dear , I am , a seasoned computer vision specialist with a proven track record in developing real-time detection systems. I understand the intricacies of your project for a Real Time Dart Detection System and am excited to offer my expertise. With proficiency in Python/OpenCV and C++/OpenCV, I am well-equipped to meet your requirements for millisecond-level detection accuracy. My approach involves utilizing three synchronized 1080p webcams, ensuring seamless integration and reliable performance. The calibration process will be user-friendly, requiring minimal effort for optimal results. I am committed to delivering a headless service that emits JSON events promptly, meeting your expectations for accuracy and latency. I am eager to discuss further details and collaborate on this innovative project. Looking forward to the opportunity to work together. Warm regards,

@Ussama91

I'll utilize Python/OpenCV for millisecond-level detection and reliable accuracy, focusing on a quick calibration routine and a headless service with JSON event output, ensuring ≥98% hit-segment accuracy and ≤150 ms latency, adapting to the proposed budget. Waiting for your response in chat! Best Regards.

@AZTURK1

Hi there, I understand you need a headless, multi-camera dart scoring service with millisecond detection and WebSocket events , I can deliver a robust Python/OpenCV or C++/OpenCV implementation tuned for ≤150 ms end-to-end latency and ≥98% segment accuracy. - Multi-camera calibration & synchronization routine that runs in ≤2 minutes (pub-friendly) - Real-time detection pipeline (background subtraction, sub-pixel impact localization, multi-view triangulation) and JSON WebSocket emitter - Source, build/run docs, calibration printables, API spec, test client, and performance report with annotated test footage Skills: ✅ Python ✅ C++ / OpenCV ✅ multi-view geometry & triangulation ✅ WebSocket real-time event API and headless deployment ✅ CUDA acceleration / performance tuning for low latency ✅ accuracy validation and robustness under lighting variance Certificates: ✅ Microsoft® Certified: MCSA | MCSE | MCT ✅ cPanel® & WHM Certified CWSA-2 I can start immediately and deliver the code, docs and report within the proposed timeline. Which mid-range PC specs will you use for acceptance testing (CPU, RAM, GPU), and do you have preferred webcam models for synchronization? Best regards,

@jonathangleon

Hi there, This is exactly the kind of real-time vision problem I enjoy building. I can develop a headless dart scoring service using Python/OpenCV (or C++ if lower latency is required) that detects dart impact, maps the tip to board coordinates, calculates the correct segment, and instantly emits a WebSocket JSON event like: `{ "x":…, "y":…, "ring":"double", "number":20, "score":40 }` Approach: • Use multi-camera homography to map each webcam to a unified board plane • Detect impact via motion/frame differencing and confirm dart tip position • Convert (x,y) to polar coordinates to determine ring and sector • Publish real-time events through a lightweight WebSocket server Calibration will be fast (no chessboard required), using simple board reference alignment that a pub user can complete in under 2 minutes. Deliverables include full source code, build instructions, calibration workflow, WebSocket API spec + test client, and a performance report covering accuracy and latency. Ready to discuss lighting handling and multi-camera fusion to reach ≥98% accuracy and ≤150ms latency. Best regards, Jonathan