Browsing by Author "Güneş, Ahmet"

Now showing 1 - 11 of 11

Citation Count: 2
4-Stage Target Detection Approach in Hyperspectral Images
(IEEE Computer Society, 2018) Ozdil,O.; Gunes,A.; Esin,Y.E.; Ozturk,S.; Demirel,B.; Department of Mechatronics Engineering
Practical target detection systems require an automatic way to detect targets with high accuracy. Detection errors is not tolerable and they should be reduced as much as possible. In classical detection systems, generally single target detection algorithm is performed and the result will be evaluated according to the thresholding techniques. However, in these uncontrolled systems, false alarm rate strongly depends on the thresholding technique success. It is very hard to find a general and constant threshold value for images taken at different conditions and practical detection systems needs reliable threshold value. In this paper, we propose a new multi-stage target detection system which is the combination of different detection algorithms and thresholding technique. This system compose of 4-stages, i.e. namely 1-initial target detection (ACE, GLRT), 2-adaptive Constant False Alarm Rate (CFAR) thresholding, 3-spatially grouping, 4-statistical confidence operation. This system configuration removes the need for interactive user and it automatically implements confirmation and rejection steps. Moreover, this system can be used both for pure pixel and subpixel target detection purposes and it reduces computational processing time considerably with the implementation of consequtive processing stages. © 2018 IEEE.
Citation Count: 3
Comparison of Target Detection Performance for Radiance and Reflectance Domain in VNIR Hyperspectral Images
(Ieee, 2019) Ozdil, Omer; Gunes, Ahmet; Esin, Yunus Emre; Demirel, Berkan; Ozturk, Safak; Department of Mechatronics Engineering
In 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.
Citation Count: 1
Comparison of Target Detection Performance for Radiance and Reflectance Domain in VNIR Hyperspectral Images
(Institute of Electrical and Electronics Engineers Inc., 2019) Ozdil,O.; Gunes,A.; Esin,Y.E.; Demirel,B.; Ozturk,S.; Department of Mechatronics Engineering
In 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. © 2019 IEEE.
Citation Count: 6
Escaping Local Minima in Path Planning Using a Robust Bacterial Foraging Algorithm
(Mdpi, 2020) Abdi, Mohammed Isam Ismael; Khan, Muhammad Umer; Gunes, Ahmet; Mishra, Deepti; Mechatronics Engineering; Department of Mechatronics Engineering; Computer Engineering
The 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.
Citation Count: 0
4-Stage Target Detection Approach In Hyperspectral Images
(Ieee, 2018) Ozdil, Omer; Gunes, Ahmet; Esin, Yunus Emre; Ozturk, Safak; Demirel, Berkan; Department of Mechatronics Engineering
Practical target detection systems require an automatic way to detect targets with high accuracy. Detection errors is not tolerable and they should be reduced as much as possible. In classical detection systems, generally single target detection algorithm is performed and the result will be evaluated according to the thresholding techniques. However, in these uncontrolled systems, false alarm rate strongly depends on the thresholding technique success. It is very hard to find a general and constant threshold value for images taken at different conditions and practical detection systems needs reliable threshold value. In this paper, we propose a new multi-stage target detection system which is the combination of different detection algorithms and thresholding technique. This system compose of 4-stages, i.e. namely 1-initial target detection (ACE, GLRT), 2-adaptive Constant False Alarm Rate (CFAR) thresholding, 3-spatially grouping, 4-statistical confidence operation. This system configuration removes the need for interactive user and it automatically implements confirmation and rejection steps. Moreover, this system can be used both for pure pixel and subpixel target detection purposes and it reduces computational processing time considerably with the implementation of consequtive processing stages.
Citation Count: 0
Multiple underwater target bearing tracking using MemBer Filter
(Ieee, 2018) Gunes, Ahmet; Guldogan, Mehmet B.; Department of Mechatronics Engineering
Underwater acoustic vector sensors (AVS) are devices which can measure scalar pressure and three dimensional acceleration or particle velocity with only one sensor. By using these four measurements, target detection and tracking is possible. In case multiple targets exist, multi-target detection and tracking methods must be applied. Because these methods are more general, the algorithms are more involved and complex. In this framework, multi-target multi-Bernoulli (MeMBer) is a promising filter based on random finite sets (RFS) for multi-target tracking problems. In this work, for the first time in the literature, MeMBer filter is analyzed using a single underwater acoustic vector sensor in a scenario including two targets. Simulation results indicate that MeMBer filter can successfully track the targets.
Citation Count: 1
Multiple underwater target bearing tracking using MemBer filter;
(Institute of Electrical and Electronics Engineers Inc., 2018) Gunes,A.; Guldogan,M.B.; Department of Mechatronics Engineering
Underwater acoustic vector sensors (AVS) are devices which can measure scalar pressure and three dimensional acceleration or particle velocity with only one sensor. By using these four measurements, target detection and tracking is possible. In case multiple targets exist, multi-target detection and tracking methods must be applied. Because these methods are more general, the algorithms are more involved and complex. In this framework, multi-target multi-Bernoulli (MeMBer) is a promising filter based on random finite sets (RFS) for multi-target tracking problems. In this work, for the first time in the literature, MeMBer filter is analyzed using a single underwater acoustic vector sensor in a scenario including two targets. Simulation results indicate that MeMBer filter can successfully track the targets. © 2018 IEEE.
Citation Count: 3
Pseudospectral Time Domain Method Implementation Using Finite Difference Time Stepping
(Ieee-inst Electrical Electronics Engineers inc, 2018) Gunes, Ahmet; Aksoy, Serkan; Department of Mechatronics Engineering
Lagrange 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.
Citation Count: 0
Shape Recognition with Low Cost Sensors
(Ieee, 2018) Saloglu, Keziban; Hosafci, Arda; Birbilen, Merve; Bulut, Yigit A.; Gunes, Ahmet; Department of Mechatronics Engineering
This paper proposes a method to recognize the shape of some objects that have different geometrical properties using an infra-red sensor.To that end, a mechanism that has two degrees of freedom is designed. Scanning of the different objects are obtained. Noise on scanning output is removed. Finally, all the outputs for different objects are discussed to obtain the specifications to do shape recognition.
Citation Count: 0
Shape recognition with low cost sensors;
(Institute of Electrical and Electronics Engineers Inc., 2018) Saloglu,K.; Hosafci,A.; Birbilen,M.; Bulut,Y.A.; Gunes,A.; Department of Mechatronics Engineering
This paper proposes a method to recognize the shape of some objects that have different geometrical properties using an infra-red sensor. To that end, a mechanism that has two degrees of freedom is designed. Scanning of the different objects are obtained. Noise on scanning output is removed. Finally, all the outputs for different objects are discussed to obtain the specifications to do shape recognition. © 2018 IEEE.
Citation Count: 10
TobSet: A New Tobacco Crop and Weeds Image Dataset and Its Utilization for Vision-Based Spraying by Agricultural Robots
(Mdpi, 2022) Alam, Muhammad Shahab; Alam, Mansoor; Tufail, Muhammad; Khan, Muhammad Umer; Gunes, Ahmet; Salah, Bashir; Khan, Muhammad Tahir; Mechatronics Engineering; Department of Mechatronics Engineering
Selective 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.