Hybrid AI Drone Detection System -- 4

@Fazeennazar

Hello Greetings, After reviewing your project description, I am confident and excited to work on this project for you. I have some crucial points and questions to clarify. Please leave a message in the chat to discuss this, and I can share my recent work that is similar to your requirements. I am excited to hear from you soon. Thank you!

@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

@amirrmusavifr

Hi, I’m an AI expert with professional experience in computer vision, with a proven track record of working on complex image processing and AI/ML model development. With skill sets: • Algorithm Development: Strong understanding of computer vision algorithms and techniques, including convolutional neural networks (CNNs), object detection, image segmentation and feature extraction. • Model Training & fine-tuning: Develop and train machine learning models tailored for image analysis and visual data interpretation. I have worked on some well-known models like YOLO, RCNN, U-Net, Deeplab, ViT etc. • AI Integration: Implement and integrate AI models into existing software and hardware systems, ensuring high performance and scalability. • Data Analysis: Analyze and process large datasets of images and video feeds to identify patterns, trends, and insights. • Data Handling: Experience in handling and processing large datasets, including image and video data. Familiarity with data augmentation techniques and synthetic data generation. • Performance Optimization: Optimize algorithms and models for real-time processing and ensure they can handle large-scale data efficiently. • Programming Skills: Proficient in programming languages such as Python. Experience with deep learning frameworks like TensorFlow, PyTorch, or Keras. • Tools & Libraries: Proficiency with OpenCV, scikit-image, and other relevant libraries. Experience with version control systems like Git.

@Muhammadzeesha59

Hello Sir/MAM I am a skilled full stack developer. Having rich experience in Java , C++ , C , C# , Python , Eclipse , Sql , Mysql , .Net ,Oracle , Object Oriented Programming , Data Structure , Algorithms . I have a perfect grip on “Artificial Intelligence” “Automation” , and work in “Machine Learning” Deep Learning ”. My track record as demonstrated in my 100% job completion and 5-star review rating showcases My ability to deliver exceptional results on time and with utmost quality I believe that my skill set makes me the ideal candidate for this project Please come on chat we will discuss more about this I will be waiting for your reply . Thanks and Best Regards

@quan9824

Hi, I can build your full multimodal drone detection pipeline combining computer vision (YOLOv8) and RF signal classification into a single fused AI system with reproducible training and evaluation. For the vision branch, I will fine-tune YOLOv8 in PyTorch using Anti-UAV and VisDrone datasets with proper augmentation and evaluation setup. For the RF branch, I will convert SDR/IQ data (HackRF/RTL-SDR + RadioML dataset) into spectrograms and train a CNN model in PyTorch or TensorFlow depending on performance. Once both models are trained, I will implement a modular fusion layer that combines outputs using either a rule-based logic system or a lightweight MLP, designed so it can be easily swapped or extended later. The system will include complete training scripts, inference pipelines optimized for GPU (RTX 3060) and CPU fallback, and an evaluation module that outputs Accuracy, Precision, Recall, False Alarm Rate, plus confusion matrices and ROC curves. Everything will be packaged in a clean Git repo with a one-command setup and detailed README to fully reproduce results. Best, Doan

@devanshkhandekar

Hi,I am a seasoned Applied ML Engineer(6+ yoe) & I can build this as a clean,reproducible dual-branch drone detection pipeline with vision + RF fusion,keeping the implementation practical & well-documented. My approach would be: >>Vision branch: fine-tune YOLOv8 on Anti-UAV first,& use VisDrone only if the selected labels fit the target use case.I’ll convert datasets to YOLO format,train lightweight YOLOv8n/s models,export weights,& provide frame-level detection metrics >>RF branch: process HackRF/RTL-SDR or RadioML IQ captures into spectrograms using STFT/log-power transforms,then train a compact CNN in PyTorch/Keras. The RF branch will output confidence scores per burst/window >>Fusion layer: start with an explainable rule-based fusion module combining vision confidence + RF confidence,because public vision/RF datasets are usually not synchronized. If paired RF+video samples are available,I can add a small MLP fusion model later >>Evaluation: provide scripts/notebooks that compute Accuracy,Precision,Recall,False Alarm Rate,confusion matrices & ROC curves for each branch & the fusion output >>Deployment-ready repo: dependencies,training script,saved weights,inference scripts,README I’ll also clearly document limitations,especially that RadioML is useful for RF modelling but not a true drone-specific RF dataset unless real drone captures are provided.

@divyam12345

Dear Sir/Madam, I have experience in computer vision, RF signal processing, and deep learning. I can build this hybrid pipeline with YOLOv8 for vision, a CNN for RF spectrograms, and a clear fusion layer. I am confident I can deliver a clean, reproducible system. Let’s connect in the chatbox to discuss the project further, including the budget and timeline. I am ready to work with you, please connect in the chatbox for further discussions. Thank You. Dr. Divya.

@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.

@ABDOKAMA

السلام عليكم ورحمة الله وبركاته I propose a hybrid AI pipeline that integrates Computer Vision (CV) and Radio Frequency (RF) sensing to detect low-flying drones with high precision. This system leverages my extensive experience in small object detection and drone technology. 2. Technical Approach Vision Branch: Utilizing YOLOv8 fine-tuned on Anti-UAV and VisDrone datasets to identify small objects in real-time. RF Branch: A CNN architecture trained on spectrograms from HackRF / RTL-SDR captures. Intelligence Fusion: A lightweight MLP (Multi-Layer Perceptron) or rule-based logic to merge scores from both branches for a final detection decision. 3. Deliverables Clean, reproducible training and inference scripts. Saved model weights optimized for single GPU (8GB VRAM) or CPU. Comprehensive evaluation reports: Accuracy, Precision, Recall, and False Alarm Rate, including ROC curves and confusion matrices.

@shadabkhan92

As a team focused on the delivery of production AI systems that have proven track records, we are confident about our capacity to bring your ideal hybrid AI drone detection system to reality. We have immersed experience in all elements required for your project. For instance, we are skilled in operating with PyTorch and TensorFlow frameworks, scikit-learn, and seaborn/matplotlib for data processing, design, and visuals. This will ensure the creation of clean, reproducible training scripts for both branches and the fusion layer as well as saved model weights which aligns with your expectations. Moreover, my team's RF competence combined with our machine learning skillset sets your project up for success. We have had multiple successful exposures to Radio Frequency projects. With this experience, we are undoubtedly well-placed to achieve the CNN model on spectrograms component of your work in either TensorFlow/Keras or PyTorch - whatever option you find more suitable. Lastly, our wide-ranging experience across various technologies and platforms including Odoo ERP end-to-end implementation and IoT device design will be highly advantageous for the successful development and integration of your AI drone detection system into existing workflows and hardware architecture. This combined strength makes us the ultimate choice for accomplishing a remarkable drone detection system that operates real-time and as efficient as you desire. I look forward to bringing your vision to life.

@qualityxenter

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@Feriver

Hello, I will develop a complete hybrid AI drone detection pipeline that fuses computer vision and RF signal intelligence into a unified, reproducible system with strong evaluation and deployment readiness. The vision branch will be implemented using YOLOv8 fine-tuned on Anti-UAV and VisDrone datasets for real-time drone detection. The RF branch will process HackRF/RTL-SDR captures and DeepSig RadioML data by converting signals into spectrograms and training a CNN model for classification. Both pipelines will be optimized to run efficiently on a single GPU (8GB VRAM) with CPU fallback support. I will design and implement a modular fusion layer that combines outputs from both branches using either a rule-based decision engine or a lightweight MLP, depending on performance stability. The fusion logic will be fully documented and interchangeable to allow future experimentation. Evaluation will include Accuracy, Precision, Recall, and False Alarm Rate, computed across held-out splits for both vision and RF datasets. I will also generate confusion matrices and ROC curves exported as PNG/PDF. The final deliverables will include clean training scripts, inference pipelines, saved weights, and a reproducible evaluation notebook, all packaged in a structured Git repository with one-command setup. A concise technical report (2–3 pages) will explain preprocessing, model selection, fusion strategy, and performance results. Thanks, Asif

@huzaifarizwan995

I’d be a strong fit for this project because it sits right at the intersection of computer vision, RF signal processing, and multi-modal fusion, and the key challenge here is not just training two models—but designing a clean, synchronized pipeline and a reliable fusion strategy that performs well under real-world conditions. My approach would be to build this as a modular, reproducible system, so each component (YOLOv8, RF CNN, fusion layer) can be trained, evaluated, and swapped independently. ? How I would structure the system 1. Vision Branch (YOLOv8 – PyTorch) Fine-tune YOLOv8 on Anti-UAV + VisDrone Standardize annotations and balance classes Output per-frame: bounding boxes confidence scores (drone vs non-drone) 2. RF Branch (CNN on Spectrograms) Preprocess HackRF / RTL-SDR + RadioML: FFT → spectrogram conversion normalization + augmentation Train a CNN (PyTorch preferred for consistency) Output per-burst: probability of drone signal presence 3. Fusion Layer (Key Component) I’d implement two interchangeable strategies: Option A — Rule-Based (Baseline + Interpretable) Threshold-based logic: Vision_conf > T1 AND RF_conf > T2 → Drone detected Weighted fallback rules Option B — Lightweight Neural Fusion (Recommended) Small MLP taking: Vision confidence RF confidence optional temporal smoothing features Output: final detection probability Both approaches will be cleanly abstracted so you can swap them easily.

@noursherif030

With a foundation in Software Engineering and Information Systems, I've spent over five fruitful years navigating complex projects involving programming, visualizing data, computer vision, and applying machine learning models. My experience aligns perfectly with your project's multi-faceted nature. In fact, I have successfully developed multiple hybrid AI systems merging computer vision and RF detection, equipped with the specific models you've mentioned – such as YOLOv8 in PyTorch and CNN for spectrograms using TensorFlow or Keras. When working on projects similar to yours, I always emphasize clean and reproducible code. As a result, I'll provide you with well-documented training scripts for all branches and an evaluation notebook that comprehensively elaborates on the data preprocessing stages, model selections made & fusion logic adopted, thus ensuring easy replication and maintenance of the system. Additionally, as a cybersecurity specialist by trade, you can rely on me to secure every aspect of this project including the codebase prior to delivery; ensuring your data's confidentiality.

@PyMLExpert1

With a proven track record in developing efficient and scalable AI solutions, I am well-positioned to help you build your Hybrid AI Drone Detection System. My expertise in Computer Vision and Deep Learning, coupled with my extensive background in Python and Machine Learning will guarantee a project that meets your expectations. Specifically, I have hands-on experience working with machine learning models like YOLOv8 using PyTorch and building CNN architectures on TensorFlow, Keras, or PyTorch for projects similar to yours. I have also successfully implemented fusion layers, both rule-based logic trees and neural networks, resulting in models that delivered accurate predictions while maintaining low latency à crucial requirement you've highlighted. Additionally, my proficiency in Git version control ensures that I can create a well-organized repository encapsulating all the deliverables of the project - from the training scripts to the evaluation notebooks - effectively enabling easy replication of results. For complete transparency, I intend to document not just the code but the entire decision-making process such as data preprocessing, model choices, fusion logic and sharing this information through a brief write-up. Overall, I believe my blend of technical acumen and commitment to detail sets me perfectly

@jeremiasg17

Hello! I've built a similar AI pipeline that fused computer vision and RF data, achieving a significant boost in detection accuracy while reducing false alarms. I can share the implementation details in chat if you're interested. For your project, I’d approach it by fine-tuning YOLOv8 for the vision branch and training a CNN on the spectrograms for the RF data. The fusion logic could be a lightweight MLP that efficiently combines the outputs, allowing for easy adjustments later. What specific metrics do you envision being most critical for your application? If you’d like, we can start with a quick call or a small test task to align on the details. If you’re open, I can share my similar build and we can see if it fits your needs.

@MuhammadAsad108

✔ I deliver 100% work — 99.9% is not for me. ✔ Workflow Diagram Video/RF Input ⟶⟶ Preprocessing (Frames + Spectrograms) ⟶⟶ YOLOv8 Detection ⟶⟶ RF CNN Classification ⟶⟶ Feature Fusion Layer ⟶⟶ Decision Engine ⟶⟶ Metrics & Evaluation Key Highlights ✔ Dual-modality system — vision + RF fusion for robust detection. ✔ YOLOv8 pipeline — fine-tuned on Anti-UAV & VisDrone datasets. ✔ RF intelligence — CNN on spectrograms (HackRF / RTL-SDR / RadioML). ✔ Fusion logic — rule-based or lightweight MLP (modular & replaceable). ✔ Optimized inference — <1s latency on RTX 3060 / CPU fallback. ✔ Full evaluation — Accuracy, Precision, Recall, False Alarm Rate. ✔ Visual outputs — confusion matrix + ROC curves (PNG/PDF). ✔ Reproducible repo — one-command setup with pinned dependencies. Best Regards, Asad AI Engineer | Computer Vision + RF Signal Processing | Deep Learning

@pamelah35

Hello, I am a machine learning engineer with deep experience in computer vision (YOLOv8, PyTorch) and RF spectrogram classification (CNNs in TensorFlow/Keras), and I will build your hybrid drone detection pipeline, fine-tuning YOLOv8 on Anti-UAV and VisDrone datasets for vision branch, and training a custom CNN spectrogram classifier on HackRF/RTL-SDR captures plus RadioML corpus for RF branch. I will implement a rule-based fusion layer (e.g., confidence thresholding, weighted voting, or logic tree) that combines both branch scores to produce final drone presence decisions, and evaluate the full system on held-out test splits to report Accuracy, Precision, Recall, and False Alarm Rate. Deliverables include clean training scripts, saved model weights (compatible with 8GB GPU or CPU fallback), an evaluation notebook exporting confusion matrices and ROC curves, and a 2–3 page write-up covering preprocessing, model choices, fusion logic, and observed performance. I guarantee end-to-end inference latency under 1 second per sample on an Nvidia 3060 equivalent, and will package all code in a Git repo with a single-command replication README. Best regards.

@mayr81

Hello there! Your hybrid AI drone detection system sounds like an exciting challenge, and I’ve worked extensively with YOLO pipelines, SDR preprocessing, and RF-based CNN classifiers. I can help you build clean, reproducible training and inference scripts for both sensing branches and design a transparent fusion layer that you can easily swap later. I’ll ensure all metrics, confusion matrices, and ROC curves are generated with clear plots and that the full repo runs with a single command as you expect. The end-to-end pipeline can be optimized to meet the sub‑second latency requirement on a 3060 while keeping VRAM usage low. Before starting, I want to be sure I fully understand how you want the fusion logic documented. Best regards!