This project titled Efficient Wood Surface Detection Using YOLO Deep Neural Networks focuses on developing an intelligent detection system for identifying wood surface features using advanced object detection techniques. The system employs YOLOv9 and YOLOv11 algorithms to analyze image data and detect patterns or surface irregularities with precision. The Wood Defects Dataset from Roboflow is used to train and validate the models. The project integrates a web-based platform built with Flask, providing modules such as Home, Register, Login, Detection, Live, and Logout for user interaction. The deep learning models extract features using convolutional layers and predict surface conditions in a single processing step, ensuring efficient computation. The implementation demonstrates the effectiveness of YOLO-based networks in enhancing detection accuracy and processing efficiency. The outcome of this project provides a structured framework for intelligent surface detection and establishes a foundation for further research on automated image-based analysis systems.

Keywords: YOLO, Deep Learning, Wood Surface, CNN, Flask, Object Detection, YOLOv9, YOLOv11, Image Analysis, Surface Detection.