Derin Öğrenme Algoritması (YOLO) ile Dinamik Test Süresince Süspansiyon Parçalarında Çatlak Tespiti

Abstract

Nowadays, the use of Deep Learning methods has increased in many areas. Artificial Intelligence, which includes deep learning, is the ability of a computer or a computer-controlled machine to make a decision similar to intelligent creatures. In short, Artificial Intelligence enables the computer to think like a human. Deep Learning is a field of study that includes neural networks with one or more hidden layers and similar machine learning algorithms. In other words, in deep learning, the computer uses at least one artificial neural network and obtains new data from the data it has with different algorithms. There are many algorithms used in the Deep Learning field. Among these algorithms, YOLO (You only look once) algorithm and Darknet model provide higher FPS (Frame Per second) due to high processing speed and give clearer results. For this reason, the YOLO algorithm has been preferred in the application. Trials have been made for 4 different versions of the algorithm, the results have been compared, the best result in terms of detection accuracy and speed has provided in the Version-4 algorithm. Using Python programming language libraries such as OpenCV, NumPy, and SciPy, the number of detected deformities were determined, their detection moments were recorded, and the test system was stopped by providing information exchange with the test control system of these algorithms. A large dataset has been created for deformations and this data set has been trained and implemented with 4 different algorithm versions. The performances of the suspension system components produced in automotive reflectivity are tested with dynamic tests. In these tests, the test system is stopped under human control until the parts are subjected to plastic deformation or in case of crack formation that is a sudden breakout. However, in this case, the first moment of deformation of the parts cannot be detected and at the same time causes a waste of time. In this study, the use of deep learning, image processing and Python libraries in object detection has been examined in detail, the application has been created, and the results have been obtained by using the Python program and Artificial Neural Networks.