Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
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Master Thesis Row Following and Altitude Estimation With Uav Images for Agricultural Fields(01. Izmir Institute of Technology, 2023) Baştanlar, Yalın; Baştanlar, Yalın; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyTraditional 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.Master Thesis Improvement on Motion-Guided Siamese Object Tracking Networks Using Prioritized Windows(01. Izmir Institute of Technology, 2021) Ünlü, Ünver Can; Baştanlar, Yalın; Baştanlar, Yalın; 01. Izmir Institute of Technology; 03.04. Department of Computer Engineering; 03. Faculty of EngineeringIn recent years, there has been significant progress in Visual Object Tracking with evolutions of both computers and learning algorithms, especially in Neural Networks. Therefore, we obtain better results by combining Neural Networks and traditional tracking methods such as Kalman Filter and Correlation Filters. SiamFC is an example of such algorithms because SiamFC combines Siamese Neural Networks and Correlation Filters. SiamFC is open to development because it does not have an online learning process. An example of the improved SiamFC is Kalman-Siam that combines Kalman Filter and Multi-feature SiamFC. Kalman-Siam uses Kalman-Filter to solve the occlusion situation problem by processing the target's previous motion trajectory. Therefore, the tracking can fail in other complex scenarios for Kalman-Siam. One of the methods for solving such problems is detecting this situation and starting the re-tracking process as we used in this research. Also, we used a parameter calculated on the response map after the correlation operation in SiamFC to detect these situations. First, our algorithm generates possible prioritized search windows. Then, it runs in a specific order of priority for these generated search windows surrounding the target's last known location. We named this process Adaptive Window Search that starts from the highest priority search windows and continues until the lowest search windows do not exist. Therefore, we named our algorithm Adaptive-Kalman-Siam. We demonstrated more successful results on commonly used datasets. Adaptive-Kalman-Siam tracks an object better than SiamFC and Kalman-Siam in Background Clutters, Fast Motion, Motion Blur, and Occlusion complex tracking scenarios.