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Row Following and Altitude Estimation With Uav Images for Agricultural Fields
(01. Izmir Institute of Technology, 2023) Yörük, Burak; Baştanlar, Yalın
Traditional methods in agriculture involve the use of tractors; however, more than 10\% of the planted fields suffer from harvest losses due to these vehicles. Moreover, tractors cannot enter all agricultural lands, thus reducing the available field for planting. After heavy rainfall, mud and other effects prevent these vehicles from accessing arable field, and processes such as crop spraying take significantly longer. In the past, aerial spraying methods using high altitude aircraft were attempted to overcome these problems; however, this method was banned in many areas due to the insufficient altitude and the harmful effects of chemical dispersion outside the fields. Nowadays, UAVs present a better alternative, and aerial spraying methods are regaining popularity. However, these vehicles can still cause errors when flying with a human operator, and their flight times are limited due to inadequate battery capacity. Therefore, the development of UAVs capable of autonomous flight reduces operator costs. However, during flight, liquid changes in the pesticide tanks hinder the UAV's ability to spray pesticides autonomously at a fixed altitude and prevent unwanted pesticide dispersion in undesirable rows. The thesis study provides following of plant rows on UAV images and making altitude estimation from camera images. In this way, it ensures that the UAVs in agricultural areas can stay at a fixed altitude for appropriate spraying and irrigation and prevents the spread of pesticides to unwanted rows.
Detection and Localization of Motorway Overhead Directional Signs by Convolutional Neural Networks Trained With Synthetic Images
(Izmir Institute of Technology, 2019) Hekimgil, Hakan; Baştanlar, Yalın
Image classification, object detection and recognition have gone a long way in the last decade. The competitions, starting with ImageNet, have shown that various improving implementations of Artificial Neural Networks are the best Machine Learning techniques at the time for such tasks. However, machine learning methods require much training data and the such data for image related tasks come at a cost in terms of time and effort, if it can be obtained at all. When training data is scarce or not representative of the whole target set, synthetic data and data augmentation methods are used to increase the training data using what is already available. This thesis work shows that when the target classification images have a structure, even a loose one, it is still possible to use machine learning methods, deep learning in this case, without any real data to begin with and still produce a good detection model. In this work, a Convolutional Neural Network model is trained to detect and localize informative motorway lane direction signs. Starting with no real samples of the target images, a large computer-generated training set is created to train the model. The resulting detector can detect the required sign types with high accuracy, localizing their position by bounding boxes and categorizing them.
Shape Based Detection and Classification of Vehicles Using Omnidirectional Videos
(Izmir Institute of Technology, 2015) Karaimer, Hakkı Can; Baştanlar, Yalın
To detect and classify vehicles in omnidirectional videos, an approach based on the shape (silhouette) of the moving object obtained by background subtraction is proposed. Different from other shape based classification techniques, the information available in multiple frames of the video is exploited. Two different approaches were investigated for this purpose. One is combining silhouettes extracted from a sequence of frames to create an average silhouette, the other is making individual decisions for all frames and use consensus of these decisions. Using multiple frames eliminates most of the wrong decisions which are caused by a poorly extracted silhouette from a single video frame. The vehicle types which are classified are motorcycle, car (sedan) and van (minibus). The features extracted from the silhouettes are convexity, elongation, rectangularity, and Hu moments. Three separate methods of classification is applied. The first one is a flowchart based (i.e. rule based) method, the second one is K nearest neighbor classification, and the third one is using a Deep Neural Network. 60% of the samples in the dataset are used for training. To ensure randomization, the procedure is repeated three times with the whole dataset split each time differently into training and testing samples (i.e. three-fold cross validation). The results indicate that using silhouettes in multiple frames performs better than using single frame silhouettes.

Master Degree / Yüksek Lisans Tezleri

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