Improved YOLOv10-m: A High-Accuracy and Real-Time Object Detection Framework for Precision Applications

🔍 Improved YOLOv10-m: A High-Accuracy and Real-Time Object Detection Framework for Precision Applications Improved YOLOv10-m is an advanced deep learning–based object detection framework derived from the YOLOv10 architecture, specifically designed to enhance feature representation and fine-grained object recognition. The model achieves a strong balance between detection accuracy, computational efficiency, and real-time inference performance, making it well suited for deployment in resource-constrained and real-world environments. Its performance has been extensively evaluated on a dedicated Cydia pomonella pest detection dataset, demonstrating its effectiveness in precision agriculture applications. This repository provides all necessary resources to retrain Improved YOLOv10-m on custom datasets, including full access to the model architecture, configuration files, and an annotated benchmark dataset. The modular and extensible design of the framework facilitates further optimization, transfer learning, and adaptation to domain-specific detection tasks. 🚀 Why Improved YOLOv10-m? Improved YOLOv10-m was developed to overcome limitations observed in earlier YOLO variants when applied to high-precision and domain-specific object detection problems. The architecture is optimized to capture discriminative features while maintaining low computational overhead, enabling accurate and efficient real-time inference. ✅ Key Advantages: Enhanced Backbone and Neck for richer multi-scale feature representation. Real-Time Performance: Optimized architecture with reduced FLOPs. Modular and Scalable Design: Facilitates customization and extension to new application domains. 🧠 Application Domains Improved YOLOv10-m is applicable to a wide range of precision-critical tasks, including: 🌾 Agriculture – Detection of pests and plant diseases in precision farming. 🏥 Medical Imaging – Accurate identification of anomalies in radiology or histopathology. 🚗 Autonomous Driving – Reliable detection of pedestrians, vehicles, and obstacles. 🤖 Robotics – Real-time scene understanding and navigation. 🔬 Scientific Research – Automated identification of biological or physical entities. 📦 Repository Contents This repository includes: - ultralytics/Myfiles/Imp_yolov10m.yaml – Full Improved YOLOv10-m model structure (for easy modification or transfer learning).- ultralytics/Myfiles/Cydia_Po_der.yaml – Example configuration file for dataset structure and class labels. - Dataset/Cydia_Po – Curated pest detection dataset with images and YOLO-formatted annotations.- Improved_YOLOv10_Cydia_Po.ipynb – Jupyter Notebook to track the stages of uploading files and training the model on custom data.