Classification of Human and Vehicles with The Deep Learning Based on Transfer Learning Method

Abstract

There has been a significant increase in the use of deep learning algorithms in recent years. Convolutional neural network (CNN), one of the deep learning models, is frequently used in applications to distinguish important objects such as humans and vehicles from other objects, especially in image processing. After the ImageNet Large Scale Visual Recognition Competition (ILSVRC) in 2012, the use of ESA in applications is becoming quite common. With the development of image processing hardware, the image processing process is significantly reduced. Thanks to these developments, the performance of studies on deep learning is increasing. In this study, a system based on deep learning has been developed to detect and classify objects (human, car and motorcycle / bicycle) from images captured by drones. Two datasets, the image set of Stanford University and the drone image set created at Afyon Kocatepe University (AKÜ), are used to train and test the deep neural network with the transfer learning method. Training and testing processes are carried out using a total of 3841 images, 2591 from the Stanford dataset and 1250 from the AKÜ dataset. The precision, recall and f1 score values are evaluated according to the process of determining and classifying human, car and motorcycle / bicycle classes using GoogleNet, VggNet and ResNet50 deep learning algorithms. According to this evaluation result, high performance results are obtained with 0.916 precision, 0.895 recall and 0.906 f1 score value in the ResNet50 model.

Keywords

• deep learning
• object detection
• cnn
• derin öğrenme
• nesne tespiti
• cnn