Hybrid AI Drone Detection System

@Rekhathakur

Hello I have thoroughly reviewed your project description and am confident in my ability to assist you in completing it successfully. I believe it would be highly beneficial to delve deeper into the specifics of the job to determine the most effective way forward. I am open to scheduling an interview at your convenience, and I genuinely appreciate the chance to collaborate with you on this project. Your response is eagerly anticipated, and I'm excited about the prospect of working together. Thank you for considering my proposal. Looking forward to your prompt reply! Best regards Rekha!!!

@markoa7

As an experienced electronic hardware and firmware engineer, I understand the intricacies and complexities of developing reliable and high-performance electronic systems. My specialization in power supply circuits, multilayer designs, wireless technologies, and signal processing aligns perfectly with the work required for your project. In my past projects, I have created innovative solutions with strong RF design capabilities, which include WiFi, Bluetooth, and RFID applications that might prove handy for the project. Moreover, my proficiency in programming microcontrollers such as STM32, DSPIC, ESP32 and ESP8266 with various communication protocols like UART, SPI, Ethernet, USB, CAN and MODBUS enables seamless integration for robust data streaming— perfect for merging disparate information streams as you envision. Overall, I am confident in my ability to design an effective proof-of-concept for detecting drones via blending sight and RF cues that meets all your metrics: accuracy, precision recall and false alarm rate. My solutions are always characterized by sharp attention to detail— for your project that means well-commented reproducible code and comprehensive performance report including confusion matrices and insightful discussion of failure cases. Let's combine our skills to create a powerful tool to secure your facility!

@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

@mubeenm5026

With over a decade of experience in AI and automation, my team at Web Crest is uniquely qualified to take on your Hybrid AI Drone Detection System project. I have a deep understanding of AI, including training and fine-tuning models such as YOLOv8, as well as expertise in RF analysis and CNNs for signal processing. This proven track record would be crucial given the sensitive nature of this specific project. What sets us apart is our ability to develop solutions that move seamlessly from laptop prototypes to field-tested hardware. We create innovative, yet practical, designs with clean, well-commented code that can be quickly implemented and scaled. Our expertise in Python, PyTorch, and standard CV/RF libraries will ensure a robust and replicable solution for you. Finally, our company’s client-centric approach means that we prioritize clear communication and tailor our solutions to meet your needs precisely. I understand the urgency of your timeline and assure you that we are ready to start right away and iterate quickly to get you meaningful results early in the project. Trust Web Crest to deliver benchmarked, reliable detection system with high-performance levels in terms of Accuracy, Precision, Recall, and False-Alarm Rate for your sensitive site security requirements. Let us build the future together!

@usamae

I'm experienced in developing AI systems combining vision and RF cues for drone detection. I have expertise in training YOLOv8 with Anti-UAV and VisDrone data, as well as building CNN models using DeepSig RadioML and SDR captures. I can seamlessly merge these streams and optimize for Accuracy, Precision, Recall, and False-Alarm Rate. My clean, well-commented Python code ensures reproducibility. I'm ready to start immediately and deliver robust solutions meeting stringent performance benchmarks.

@GdevDataSceince

Hi there, I am a Data Scientist and am a professional responsible for extracting actionable insights and knowledge from large volumes of data. As an experienced Data Scientist in the field of machine learning, I am highly proficient in Python and have a deep understanding of algorithms and data structures. My skills make me a great fit for your project as I can guide you through comprehensive coverage of data structures and algorithms while providing patient and thorough explanations. I have over 12-plus years of experience with Python Library Pandas, Karas, TensorFlow, NumPy, PyCharm, Py torch, Open CV, NLP, and others. With over a decade's worth of experience under my belt, including expertise in NLP, Neural Networks, CNNs, RNNs, LSTM, GANs just to mention a few, I can provide you not only with knowledge but also how to apply it efficiently. Partnering with me ensures you have a patient, knowledgeable and skilled tutor who is dedicated to your success in this field. My top priority is to provide a high quality of work, https://www.freelancer.com/u/GdevDataSceince Let's discuss this further via chat, and I'll start your project right now. Thanks Gdev

@denisSolution

Hello client, I'm Denis Redzepovic, an experienced developer with expertise in Python, Signal Processing, Computer Vision, Electronics, Arduino, Deep Learning, Convolutional Neural Network and Software Architecture. I have worked extensively on diverse Python projects, ranging from backend development and automation to data processing and API integrations. My deep understanding of Python’s libraries and frameworks allows me to build efficient, scalable, and maintainable solutions. I pay close attention to code quality and performance to ensure your project runs flawlessly. With my solid experience, I’m confident I can deliver results that exceed your expectations. I focus on writing clean, maintainable, and scalable code because I know the difference between 99% and 100%. If you hire me, I’ll do my best until you’re completely satisfied with the result. Let’s discuss your project details so I can tailor the perfect Python solution for you. Thanks, Denis

@quan9824

https://www.freelancer.com/projects/raspberry-pi/Powered-Monitoring-Prototype-Development/reviews ## EXPERT ##(Python and Raspberry PI, Machine learning) Hello, How are you? I’ve completed several raspberry pi projects before successfully. Recently, I developed Smart Dashcam using Raspberry PI and USB Dongle in Netherland. You can check this in my portfolio. I can upload my previous works too.. I am sure and I can start immediately. Awarding me will be the fastest way to complete your task with the best rates possible. THANK YOU.

@AvinaHSinha7

Hi there, I can start immediately and deliver a clean, reproducible proof-of-concept that fuses vision + RF for real-time drone detection. On the vision side, I’ll fine-tune YOLOv8 using Anti-UAV and VisDrone, with careful dataset balancing and augmentation for long-range targets. In parallel, I’ll build an RF CNN trained on DeepSig RadioML spectrograms plus your SDR captures (HackRF / RTL-SDR), tuned to flag drone-class emissions with low false alarms. Once both streams are stable, I’ll implement a fusion layer—starting with a fast, interpretable rule-based approach and optionally a lightweight neural fusion head if benchmarks show a clear gain. Everything will be written in Python/PyTorch, well-commented, and structured so it can move from laptop to field hardware without refactoring. You’ll receive trained weights and notebooks for both models, a documented fusion module, an end-to-end inference script (live or recorded video + I/Q), and a performance report covering Accuracy, Precision, Recall, and False-Alarm Rate, including confusion matrices and failure analysis. I’m used to tight timelines and iterative delivery, and I’ll keep the focus on speed, robustness, and deployability. Regards, Avinash

@vasudharao9

Hi, I am a Computer Science graduate from UC Berkeley with a specialization in Artificial Intelligence. I have more than 10 years of experience building and developing AI solutions. I can help you with this project. Message me to discuss this further thanks.

@DigitaSyndicate

⚠️You are not looking for a coder. You are looking for someone who can build this properly. That is exactly why your project stood out.⚠️ Your goal to fuse real-time vision and RF detection for drone identification using YOLOv8 and DeepSig RadioML data demonstrates a commitment to precision and multi-domain integration that aligns directly with how we architect robust, scalable systems at DigitaSyndicate. At DigitaSyndicate, a UK-based digital systems agency, we build precision-engineered automation, modern web platforms, and AI-driven systems designed for performance and long-term scalability. Your need for a streamlined, reliable fusion pipeline optimized for key operational metrics fits perfectly with our approach to clean, reproducible code and adaptable deployment to edge hardware. Having delivered a multi-sensor fusion prototype for a defense client balancing accuracy and speed, I’m ready to start immediately. Can you share your main priorities and timeline so I can map out the right execution plan for you? Casper M. Project Lead | DigitaSyndicate Precision-Built Digital Systems.

@ayesha0124

Hi, I'm excited about the opportunity to create a proof-of-concept hybrid AI drone detection system that seamlessly marries vision and RF cues. With extensive experience in training YOLOv8 and building CNNs, I'm confident in my ability to fine-tune these models using your Anti-UAV and VisDrone footage alongside the DeepSig RadioML data. My approach will involve training YOLOv8 to ensure precise visual detection and implementing a tailored RF-CNN model to accurately flag drone emissions from your SDR captures. The fusion of these systems will be executed with either straightforward rules or a neural layer, depending on what achieves the best balance of speed and robustness. I understand the importance of accuracy, precision, recall, and minimizing false alarms for your sensitive site, and I’ll ensure that benchmarks reflect these metrics clearly. I’m ready to start immediately and iterate quickly to meet your tight timeline. Once we finalize the approach, I'll provide well-commented code, a detailed performance report, and documentation for future transitions to field hardware. Thanks,

@codersarenaa

Hello, I can build a working proof-of-concept for real-time drone detection by combining vision (YOLOv8) and RF-based CNN analysis. I will deliver: Trained YOLOv8 model using Anti-UAV and VisDrone data Trained RF-CNN using DeepSig RadioML and your SDR captures Simple and fast fusion logic (rule-based or lightweight neural layer) End-to-end Python inference script (video + RF → detections) Performance report covering Accuracy, Precision, Recall, and False-Alarm Rate

@Zawiya3

With my diverse skill set and extensive experience in areas such as deep learning, electronics, signal processing, and software architecture, I am well-equipped to provide you with a cutting-edge solution for your Hybrid AI Drone Detection System. My proficiency in Python, PyTorch, and a range of CV/RF libraries will ensure that the implementation is carried out efficiently and effectively. Over the course of my career, I have engaged in numerous data-intensive projects similar to this one which required the integration of multiple streams of information. On top of this, I have a good command over the technologies required like YOLOv8 and the use of RF cues for drone detection. This expertise enables me to not only train and fine-tune complex models like you require but also understand how they can be successfully merged to produce the desired outputs. Additionally, my experience in designing robust and scalable serverless solutions using AWS aligns perfectly well with your request to eventually move from laptop-based model implementation to field hardware without major rework. I understand the importance of clean, well-commented, and reproducible code - attributes necessary for your project's success - all of which you can expect from me. Therefore, by choosing me you will be selecting a proven professional who is committed to delivering accurate, precise and actionable results on tight timelines.

@ravichedwal

My work in object detection, especially using neural networks such as YOLOv8 and RF sensors, aligns excellently with your needs to merge vision and RF cues to identify drones. Given my background, I am not only well-versed in Python and standard CV/RF libraries but also quite experienced in working within the PyTorch framework; ensuring a streamlined process for both deployment and reproducibility. Moreover, your sensitivity about efficiently moving the proof-of-concept from laptop to field hardware without major rework is well-founded. I have prior expertise executing similar transitions while retaining top-tier performance, which can be highly beneficial when we transform this project from prototype to practicality. Additionally, my proficiency in dataset creation and deep understanding of artificial neural networks including CNNs will guarantee accurate, precise and low false-alarm detection by delivering reliable benchmarks on your specified metrics. With an all-encompassing skill set spanning web development, mobile development, desktop development and IoT alongside machine learning , I bring a refreshing holistic perspective towards tackling projects - identifying creative connections between them where they seem disparate at first glance. This allows me to offer comprehensive technical insight into tangible possibilities for your project. Let's start right away and make magic happen!

@ianb65

Hello, there! I can deliver a working proof-of-concept that fuses vision and RF cues for real-time drone detection and is designed to transition from laptop to field hardware with minimal rework. On the vision side, I’ll fine-tune YOLOv8 using Anti-UAV and VisDrone data with careful augmentation and class balancing, and provide trained weights plus a reproducible training notebook. In parallel, I’ll train an RF CNN on spectrograms derived from DeepSig RadioML and your SDR captures (HackRF/RTL-SDR), with clean preprocessing and labeled pipelines. For fusion, I’ll start with a fast, interpretable rule layer to establish a baseline, then iterate to a lightweight neural fusion if it materially improves robustness without adding latency. The end-to-end inference will ingest live or recorded video and I/Q streams, synchronize outputs, and emit detection events. I’ll benchmark Accuracy, Precision, Recall, and False-Alarm Rate, include confusion matrices, and discuss failure modes and mitigation. Code will be Python/PyTorch, well-commented, versioned, and reproducible. I can start immediately and iterate quickly, delivering early checkpoints for review while pushing toward field-ready performance. Best regards, Ian Brown

@redbrickamerica

Hello! As a proficient team of American Engineers, we are well-equipped to tackle your Hybrid AI Drone Detection System project. Leveraging Python, PyTorch, and cutting-edge CV/RF libraries, we will meticulously craft a working proof-of-concept. Our approach involves training YOLOv8 and RF-CNN models, merging both streams seamlessly, and ensuring benchmarked accuracy, precision, recall, and false-alarm rate. The solution will transition smoothly from laptop to field hardware, maintaining robustness and speed. Clean, reproducible code and comprehensive performance reporting are guaranteed.

@electronicsdone

Best Hybrid AI Drone Detection System Engineer ⭐⭐⭐⭐⭐ Hi Ahmed, Thanks for sharing the scope clearly. I’ve worked on AI-driven vision and RF projects where real-time detection, model fusion, and reproducible results were critical. Your project looks very doable. The goal is simple: deliver a working proof-of-concept that reliably detects drones by combining computer vision and RF cues, with clean, reproducible Python/PyTorch code. ✅ How I’ll Help You Succeed 1. Train and fine-tune YOLOv8 on Anti-UAV and VisDrone datasets for vision-based detection. 2. Develop an RF-CNN using DeepSig RadioML and SDR captures to flag drone-class emissions. 3. Fuse vision and RF streams with a rule set or neural layer for robust real-time detection. 4. Provide all deliverables: trained models, notebooks, fusion module, inference script, and performance report. ✅I’ve delivered AI-driven computer vision and RF detection projects where real-time performance, accuracy, and robust model fusion were critical. ✅ Before I start, one quick thing: please share access to your datasets and SDR captures so I can plan the training and fusion workflow. If you share that, feel free to message me, and we can align quickly. Best, Prat PCB Must Innovations

@dragomira2

Hello Ahmed, Your project for a hybrid AI drone detection system is both challenging and compelling. I’m excited by the opportunity to help build a robust, field-ready solution that blends deep learning vision and RF signal analysis. With over 8 years of hands-on experience in computer vision, RF/signal processing, and hardware deployment (Arduino, SDRs), I’ve delivered similar proof-of-concept systems—most recently, a real-time aerial object tracker using YOLOv8 and a CNN-based RF classifier for spectrum monitoring. I understand your need for a dual-stream architecture: I’ll start by fine-tuning YOLOv8 on Anti-UAV and VisDrone datasets for reliable visual detection, while developing a parallel RF-CNN using DeepSig RadioML and your SDR captures. I’ll ensure both streams are modular, well-documented, and reproducible in Python/PyTorch. For fusion, I suggest starting with a rules-based approach to guarantee speed, but I’m open to implementing a neural fusion layer if it yields better robustness after initial benchmarking. The inference pipeline will be designed for easy migration from development hardware to field deployment, with clean code and standard libraries to streamline future updates. You’ll receive all trained models, clear notebooks, reproducible scripts for live/recorded input, and a comprehensive performance report focused on the four key metrics you specified. I can begin immediately and will iterate closely with you to align on benchmarks and fuse the streams effectively. Looking forward to collaborating on this vital project.