Yapay zeka tekniklerine dayanarak köprü yüzeyi çatlak tespiti

The manual inspection of concrete bridge surfaces for cracks takes a long time and wastes a lot of materials and labor. This study examines this problem by offering a quick and automated machine-learning method for visually inspecting concrete bridge surfaces with unmanned aerial vehicles (UAVs) in large open spaces. UAVs are used to capture high-resolution photographs of concrete bridge surfaces, providing thorough coverage of the inspection area. Utilizing the potent YOLO (You Only Look Once) technique, deep learning technology, a subset of machine learning, enables single-shot detectors. Use is made specifically of the YOLO v8 model, which was very accurate machine learning techniques were used to train. Since this model is based on Convolutional Neural Networks (CNNs) with the CSPDarknet-53 backbone structure, accurate and trustworthy results are guaranteed. The suggested model has outstanding performance in machine learning-based real-time crack detection. The Deep Learning model achieves remarkable precision and correctness in crack detection with an accuracy rating of 98.54%. The deep learning model can accurately detect cracks while limiting false positives, according to the F1 score, a measure of precision and recall balance, which reaches 97%. The robustness and accuracy of the machine learning model are demonstrated by the mean average precision (mAP), a statistic frequently employed in machine learning, which is calculated to be 97.90%. With a mAP50-95 of 83.11%, the model also achieves a high mAP across various Intersections over Union (IoU) thresholds. The recall rate reaches 94.66%, ensuring a high detection rate for true cracks, while the precision rate measures an astonishing 99.41%, minimizing false alarms. Additionally, the model runs at a phenomenal 95.24 frames per second (fps), which makes it possible to use machine-learning approaches for effective real-time crack detection. This speed suits it for practical applications since it enables prompt decision-making and intervention. Our study suggests a new, effective machine-learning method for instantly detecting cracks in concrete bridge surfaces. Utilizing image collecting from UAVs, With the help of the YOLO algorithm. Keywords: Crack detection, Machine learning, Deep Learning, Unmanned Aerial Vehicles (UAVs), Convolutional Neural Networks (CNNs), YOLO v8