Browsing by Author "Gunes, Ahmet"
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Conference Object Citation Count: 3Comparison of Target Detection Performance for Radiance and Reflectance Domain in VNIR Hyperspectral Images(Ieee, 2019) Güneş, Ahmet; Gunes, Ahmet; Esin, Yunus Emre; Demirel, Berkan; Ozturk, Safak; Department of Mechatronics EngineeringIn this paper, the hyperspectral detection of targets in visible-near infrared (VNIR) images is studied. The change of radiance domain signatures in images taken in different locations, time and altitudes are analyzed. A new radiance domain detection scheme for VNIR images under 1000 m altitude is proposed. The analysis shows that the radiance domain signatures of each target, that are collected from an image taken at 10 m altitude, can be effectively used for pure pixel target detection in other VNIR images taken at altitudes between 10 - 1000 m. The proposed approach is tested using several target types and on images taken at different altitudes and environmental conditions. Our results show that target detection in radiance domain provides a cheaper, easier and effective alternative to reflectance domain, in VNIR images.Article Citation Count: 6Escaping Local Minima in Path Planning Using a Robust Bacterial Foraging Algorithm(Mdpi, 2020) Khan, Muhammad Umer; Khan, Muhammad Umer; Güneş, Ahmet; Mıshra, Deepti; Mechatronics Engineering; Department of Mechatronics Engineering; Computer EngineeringThe bacterial foraging optimization (BFO) algorithm successfully searches for an optimal path from start to finish in the presence of obstacles over a flat surface map. However, the algorithm suffers from getting stuck in the local minima whenever non-circular obstacles are encountered. The retrieval from the local minima is crucial, as otherwise, it can cause the failure of the whole task. This research proposes an improved version of BFO called robust bacterial foraging (RBF), which can effectively avoid obstacles, both of circular and non-circular shape, without falling into the local minima. The virtual obstacles are generated in the local minima, causing the robot to retract and regenerate a safe path. The proposed method is easily extendable to multiple robots that can coordinate with each other. The information related to the virtual obstacles is shared with the whole swarm, so that they can escape the same local minima to save time and energy. To test the effectiveness of the proposed algorithm, a comparison is made against the existing BFO algorithm. Through the results, it was witnessed that the proposed approach successfully recovered from the local minima, whereas the BFO got stuck.Article Citation Count: 3Pseudospectral Time Domain Method Implementation Using Finite Difference Time Stepping(Ieee-inst Electrical Electronics Engineers inc, 2018) Güneş, Ahmet; Aksoy, Serkan; Department of Mechatronics EngineeringLagrange interpolation polynomials-based Cheby-shev pseudospectral time domain (CPSTD) method is an efficient time domain solver for Maxwell equations. Although it has the lowest interpolation error among pseudospectral time domain methods, time derivatives must be calculated using higher order time derivative schemes, such as the Runge-Kutta method. The higher order time derivative methods slow down the computation speed at each step by several folds. In this letter, we show that central finite differences can be used for implementation of time derivatives in CPSTD method. Results are verified by a resonator problem.Article Citation Count: 10TobSet: A New Tobacco Crop and Weeds Image Dataset and Its Utilization for Vision-Based Spraying by Agricultural Robots(Mdpi, 2022) Khan, Muhammad Umer; Alam, Mansoor; Güneş, Ahmet; Khan, Muhammad Umer; Gunes, Ahmet; Salah, Bashir; Khan, Muhammad Tahir; Mechatronics Engineering; Department of Mechatronics EngineeringSelective agrochemical spraying is a highly intricate task in precision agriculture. It requires spraying equipment to distinguish between crop (plants) and weeds and perform spray operations in real-time accordingly. The study presented in this paper entails the development of two convolutional neural networks (CNNs)-based vision frameworks, i.e., Faster R-CNN and YOLOv5, for the detection and classification of tobacco crops/weeds in real time. An essential requirement for CNN is to pre-train it well on a large dataset to distinguish crops from weeds, lately the same trained network can be utilized in real fields. We present an open access image dataset (TobSet) of tobacco plants and weeds acquired from local fields at different growth stages and varying lighting conditions. The TobSet comprises 7000 images of tobacco plants and 1000 images of weeds and bare soil, taken manually with digital cameras periodically over two months. Both vision frameworks are trained and then tested using this dataset. The Faster R-CNN-based vision framework manifested supremacy over the YOLOv5-based vision framework in terms of accuracy and robustness, whereas the YOLOv5-based vision framework demonstrated faster inference. Experimental evaluation of the system is performed in tobacco fields via a four-wheeled mobile robot sprayer controlled using a computer equipped with NVIDIA GTX 1650 GPU. The results demonstrate that Faster R-CNN and YOLOv5-based vision systems can analyze plants at 10 and 16 frames per second (fps) with a classification accuracy of 98% and 94%, respectively. Moreover, the precise smart application of pesticides with the proposed system offered a 52% reduction in pesticide usage by spotting the targets only, i.e., tobacco plants.