YOLO object detection – Procesionaria del pino (pine processionary nests)

@ZohaibRoy

⭐⭐⭐⭐⭐ Create a YOLO Model for Detecting Pine Processionary Caterpillar Nests ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and noticed you're looking for a specialist in Computer Vision and Deep Learning. You have no need to look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects in object detection. I will create a YOLO model trained on your custom dataset to detect nests in natural images. I will ensure high recall to minimize missed detections, and I'll provide clear instructions for inference and model reuse. ➡️ Why Me? I can easily do your YOLO model training as I have 5 years of experience in object detection, working with custom datasets, and small object detection. My expertise includes Python and PyTorch, along with OpenCV for additional support. I have a strong grip on the necessary tools to ensure effective results for your project. ➡️ 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 in chat. ➡️ Skills & Experience: ✅ YOLO Object Detection ✅ Custom Datasets ✅ Small Object Detection ✅ Python Programming ✅ PyTorch Framework ✅ OpenCV Library ✅ Data Annotation ✅ Model Evaluation ✅ Image Preprocessing ✅ Offline Processing ✅ High Recall Techniques ✅ Communication Skills Waiting for your response! Best Regards, Zohaib

@nikhilgarg96

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! Nikhil

@Mahihassi

I'm your ideal candidate because I possess an extensive range of skills, technologies and experience that are directly applicable to your project's requirements. Having worked extensively in Computer Vision and Deep Learning, I have successfully trained multiple models using YOLO object detection for various projects. Even better, I have a strong background in working with custom datasets and detecting small objects just like 'pine processionary nests' you're after. I have ample experience handling RGB images from different sources including drones and field photographs- making me well-suited for your real-world forest environment dataset. My solutions prioritize high recall, ensuring that objects are detected rather than missed which aligns seamlessly with your preferences. Your project is neither basic nor generic; rather it demands specialized skills that deal with lighting variability and complex backgrounds - expertise I can bring to bear. On top of this, I am highly effective in communication, well able to provide clear instructions for executing inferences and reusing models with new data. Additionally, my credentials extend to being proficient in Python and PyTorch, which are both preferred requirements on your side. Overall, my practical experience coupled with a pragmatic approach towards technology make me the perfect candidate for this unique and challenging project.

@devendrathakur12

Hello, I built & customized large scale object detection systems for real outdoor images where objects are small, messy, and easy to miss. Looking forward to discuss your project in detail. I’m certified in computer vision and YOLO development and work daily with YOLOv8, PyTorch, and OpenCV. What I do differently is tune models for recall first, so fewer nests get missed, even in bad light or noisy forest backgrounds. I also help clean and label data in a way that really improves results. Here’s how I see it working: your images get carefully annotated, the model is trained and tested offline, and you get clear code to run it on new photos anytime. A few questions I’d like to ask: Roughly how many images are available right now? Do drone images differ a lot from ground photos? Should we accept more false positives to catch every nest? Do you want a confidence heatmap or boxes only? Any season or lighting bias in the data? Best regards, Dev S.

@Muhammadzeesha59

Having worked in the field of Computer Vision and Deep Learning for over the past 6 years, I have profound experience with YOLO object detection and custom datasets. My specialization in Image Processing makes me well-equipped to handle small objects, even those visually ambiguous. Given the complexity of your project with background variations, illumination alterations and possible false positives, my expertise becomes even more crucial. I am a proficient programmer in Python and comfortable working with libraries such as OpenCV and PyTorch, and such skills are not just restricted to suburban problems. In fact, I consider my adaptability across a range of industries as one of my strengths. This capacity has allowed me to serve clients from varied domains without compromising on quality or deliverables. Moreover, effective communication is something I hold dear and is evident from the fact that I have served clients globally since 2018. This project will not be an exception. Expect clear instructions about how to run inferences and reuse the model along with a detailed validation report. Let's work together, apply my deep learning skills on your provided dataset while upholding practicality and delivering on realistic expectations.

@quan9824

EXPERTO en (Visión Artificial y Detección, Conteo y Seguimiento de Objetos en Tiempo Real) Hola, ¿cómo estás? Revisé tus datos cuidadosamente. He completado muchos proyectos de detección, conteo y seguimiento de objetos en tiempo real antes de usar con éxito Python y YOLOv8. Puedes consultar mi historial de trabajo en mi portafolio. Estoy seguro de que este campo y yo daremos lo mejor de nosotros. Siempre he pensado: "Es tu trabajo, también es el mío". Contratarme será la manera más rápida de completar tu proyecto con las mejores tarifas posibles. GRACIAS.

@maximg10

⭐Hi, I’m ready to assist you right away!⭐ I believe I’d be a great fit for your project since I have extensive experience in YOLO object detection and deep learning. My track record includes successful projects involving custom datasets and small object detection. Having worked on similar projects, I understand the complexities of detecting pine processionary nests in natural environments with varying lighting and intricate backgrounds. The development of a YOLO model tailored to the detection of these nests will significantly aid in forest conservation efforts. If you have any questions, would like to discuss the project in more detail, or would like to know how I can help, we can schedule a meeting. Thank you. Maxim

@Julian29

¡Hola! Después de leer su solicitud, me pareció bastante interesante. Como ingeniero sénior de desarrollo full stack, puedo brindarle asistencia oportuna y de alta calidad para su proyecto. Hablemos más en detalle por teléfono o chat. Espero su respuesta positiva. Gracias

@xuezhe315

Hi, With my extensive experience in Computer Vision and Deep Learning using Python and PyTorch, I am confident in developing a high-quality YOLO object detection model tailor-made to identify pine processionary nests in natural forest environments for your project. Forest detection poses unique challenges like variable lighting, complex backgrounds, and potential false positives (e.g. bird's nests and branches), which I have successfully tackled before. I understand the importance of working with custom datasets to train models accurately, especially when dealing with small objects like the processionary nests that can be visually ambiguous. My work with custom datasets includes proficient annotation and preprocesssing techniques, ensuring optimum performance. Having worked on projects that prioritize high recall rates rather than losing important details, I will ensure the model detects as many nests as possible while balancing false positives. Importantly, I don't just deliver models but provide comprehensive solutions; hence, you can expect a fully trained model (weights) with clear Python source code along with detailed instructions on executing inferences and reusing the model with new images. My goal is to not make empty promises but provide tangible results through transparent communication at every stage of this process. Let's connect to turn your vision into an accurate reality!

@FaizalShaik80

Hola, Oscar. He revisado cuidadosamente la descripción de tu proyecto y entiendo bien el reto que supone la detección de bolsones de procesionaria en entornos forestales reales, con objetos pequeños, fondos complejos y alta variabilidad visual. He desarrollado múltiples proyectos de detección, conteo y seguimiento de objetos utilizando Python, PyTorch y YOLOv8, trabajando con datasets personalizados y casos no urbanos similares a este. Estoy acostumbrado a optimizar modelos priorizando alto recall, aceptando falsos positivos controlados cuando el objetivo es no perder detecciones críticas. Puedo encargarme del entrenamiento completo del modelo, apoyar en la anotación, aplicar preprocesado y aumentación de datos, y entregar un modelo reutilizable con métricas claras y código bien documentado. Puedes revisar mi experiencia en proyectos similares en mi portafolio. Siempre trabajo con la idea de que tu proyecto es también el mío. Contratarme será la forma más directa y eficiente de llevar este modelo a un resultado sólido y realista. Gracias, Siva

@usmanbashir12100

Hi, I can develop a custom YOLO-based object detection model to detect pine processionary caterpillar nests in real forest environments, with a focus on high recall and robustness. Approach Train YOLOv8 (PyTorch) on your custom RGB dataset Handle small, visually ambiguous objects using proper resolution, augmentation, and anchor/stride tuning Assist with or perform dataset annotation and preprocessing Optimize for recall over precision to minimize missed nests Validate with precision, recall, and mAP (no unrealistic accuracy claims) Deliverables Trained model weights Python source code (training + inference) Clear instructions for running inference on new images Validation results and discussion of false positives (e.g., bird nests, branches) Experience YOLO (v5–v8), custom datasets, small-object detection Python, PyTorch, OpenCV Experience with non-urban, natural-environment imagery I’m comfortable working offline (no real-time requirement) and communicating clearly throughout the project. Best regards, Muhammad

@huonghy89

Hola, espero que te vaya bien. Me alegra mucho poder presentarte a tu proyecto, ya que mis habilidades son muy útiles para el tuyo. Tengo experiencia práctica en el entrenamiento e implementación de YOLO (v8/v5) en conjuntos de datos personalizados para la detección de objetos pequeños/ambiguos en entornos reales complejos, utilizando Python, PyTorch y OpenCV. Configuraré un sólido proceso de entrenamiento de YOLOv8 adaptado para objetos pequeños con alta recuperación (curación de datos, aumento, estrategia de teselado/recorte, gestión de desequilibrios de clases y ajuste de umbrales/NMS). Además, apoyaré y guiaré el proceso de anotación para garantizar la consistencia de las etiquetas y reducir los falsos positivos, como anidaciones/ramificaciones. Te entregaré los pesos entrenados, código de inferencia Python limpio, instrucciones de ejecución claras y un informe de validación (precisión/recuperación/mAP + análisis de errores) para que puedas reutilizar el modelo de forma fiable en nuevas imágenes de campo/dron. Si me envías el mensaje, podemos hablar más sobre el proyecto. Gracias.

@shakilan4

Hola, Puedo entrenar un modelo YOLO (v8) para la detección de bolsones de procesionaria en entornos forestales, enfocado en alto recall y objetos pequeños, usando datasets personalizados. Entrego pesos del modelo, código en Python, métricas (precision/recall/mAP) e instrucciones claras de uso.

@kend27

Hello, I’m Ken Dimson, a Senior AI/Computer Vision Engineer with 7+ years of experience in object detection and deep learning. I have hands-on experience training YOLO (v5/v8) models on custom datasets, including small object detection in complex, natural environments. I can help you detect Thaumetopoea pityocampa bolsones in RGB images from field or drone photography. My workflow includes: dataset preprocessing and annotation support, model training with PyTorch, inference pipelines, and evaluation using precision, recall, and mAP metrics. I focus on high recall, ensuring minimal missed detections while handling false positives effectively. I will deliver: Trained YOLO model weights Python source code for inference and retraining on new images Clear instructions for running predictions and extending the model Validation results with standard metrics I’m comfortable working with non-urban, natural datasets and can adapt preprocessing and augmentation to variable lighting and complex backgrounds. Communication and realistic results are my priority. I’d love to collaborate and deliver a robust solution for your forest monitoring project. Best regards, Ken

@thomasb726

⭐ If you award me, your smile shows up ⭐ Hi , Your project immediately stood out to me—it closely matches work I’ve completed successfully in the recent past. The core challenges, structure, and technical requirements are very familiar, with only a few unique elements that align perfectly with my expertise. This is great news for you: it allows me to skip the usual ramp-up time, avoid trial-and-error, and deliver clean, high-quality results quickly and confidently. I bring hands-on experience with Machine Learning (ML), Image Processing, OpenCV, Deep Learning, YOLO, Object Detection, Computer Vision, Python and Pytorch, along with proven workflows and best practices refined through multiple similar projects. You can view a directly relevant example in my portfolio here: https://www.freelancer.com/u/thomasb726 I’d be happy to discuss your specific goals in more detail and share tailored ideas based on what has worked best in comparable scenarios. Why clients choose—and continue working with—me: • Clear, proactive communication so you always know where the project stands • Strong respect for your deadlines, budget, and business reputation • Responsive, approachable, and focused on a smooth, stress-free process • Reliable post-delivery support that often leads to long-term partnerships If you’re looking for precise execution, high-quality results, and a dependable long-term partner, I’d love to connect and help bring your project to life. Best regards, Tom

@mauriciom46

★★★★★ Hola, He analizado cuidadosamente los requisitos de tu proyecto de detección de bolsones de procesionaria en entornos forestales mediante YOLO. He desarrollado recientemente un modelo YOLOv8 para detección de objetos pequeños en imágenes naturales capturadas por dron, trabajando con fondos complejos, alta variabilidad lumínica y priorizando recall sobre precisión para no perder detecciones relevantes. Para este proyecto entrenaré un modelo YOLO con dataset personalizado, enfocado en la detección robusta de una única clase en escenarios reales de pinar. Abordaré explícitamente el reto de objetos pequeños y visualmente ambiguos mediante técnicas de anotación cuidadas, augmentations específicas, ajuste de anchors y validación orientada a alto recall. Entregaré pesos entrenados, código en Python con PyTorch, scripts de inferencia reutilizables y métricas claras de validación (precision, recall y mAP), con instrucciones prácticas para futuros usos. Estoy disponible para comenzar de inmediato y comprometido a entregar un modelo sólido, realista y bien documentado en el menor tiempo posible, sin promesas irreales y con comunicación clara durante todo el proceso. Saludos, Mauricio

@Web1devloper

As a seasoned developer with more than 8 years of experience, I specialize in Computer Vision and Machine Learning, honing my skills in projects just like yours. I have extensive expertise in YOLO object detection, particularly with small and visually ambiguous objects, making me an ideal candidate for tackling the challenge of detecting pine processionary nests. Though not essential, my past experience in non-urban projects perfectly aligns with the natural environment prerequisite mentioned in your project. Since this project calls for high recall and processing offline, I am well acquainted with models like YOLO v8. My proficiency in Python and PyTorch - both highly preferred by you - combined with my ability to work with custom datasets and execute annotations if needed make me stand out from the rest. I believe that clear instructions are quintessential for this project and rest assured that along with delivering the final trained model and source code, I provide comprehensible documentation for executing inferences and reusing the model on new images.

@Vsion2

Welcome to professional Python development services! Hi there, I'm Alema, a Python expert programmer who strives for clear code in atmospheric, numerical weather prediction, physics, and all other seminal fields. I'm ready to provide you with high-quality services. I have completed 350+ projects with a 100% Positive Rating. If you are looking for Quality work, look no further. Also, we are a team of professional workers, and we are always available 24/7 to help employers without limitations, and delivery is guaranteed on time. Your faithfully. Eng. Alema Akter

@larasatik4

Hola, He leído con detalle su proyecto para desarrollar un modelo de detección de bolsones de procesionaria del pino usando YOLO, y me siento confiada en poder aportar una solución sólida y práctica. Tengo experiencia comprobada en detección de objetos con YOLOv8 y otros modelos basados en Transformers, trabajando especialmente con datasets personalizados y detección de objetos pequeños en entornos naturales complejos. Entiendo el desafío de identificar visualmente objetos ambiguos en imágenes RGB tomadas en campo y por dron, y enfocaría en maximizar el recall para no perder ningún bolson, equilibrando bien con una limpieza adecuada para false positives como nidos de aves o ramas. Utilizaría Python junto con PyTorch y OpenCV para todo el pipeline, desde anotación y preprocesamiento hasta entrenamiento, validación y despliegue offline. Puedo entregar un modelo entrenado con los pesos, código limpio y comentado para inferencias y reutilización, además de un reporte claro con métricas clave que faciliten su evaluación técnica. Para avanzar, podríamos definir el volumen inicial del dataset y las etapas para anotación y preprocesado para ajustar tiempos. ¿Cuántas imágenes tiene disponibles inicialmente y qué tamaño aproximado tienen los bolsones en píxeles dentro de esas imágenes? Gracias por considerar mi propuesta. Sinceramente, Larasati