Research Article
BibTex RIS Cite

Kıyı bölgelerindeki denizanası yoğunluğunun İHA görüntülerinden gerçek zamanlı tespiti için kavramsal bir sistem önerisi

Year 2023, Volume: 39 Issue: 2, 192 - 203, 31.08.2023

Serkan Dereli Mehmet Okuyar Emin Güney

Abstract

Bu çalışmada, küresel ısınmaya bağlı olarak kıyı bölgelerinde meydana gelen denizanası istilasından erken haberdar olmak ve gerekli önlemleri almak amacıyla insansız hava araçlarında kullanılabilecek bir sistem önerilmiştir. Bu araştırmadaki asıl mesele, insansız sistemlerin izleme ve bilgi toplama alanındaki maharetinin denizanası istilalarını tespit etmek için kullanılmasıdır. Bunun için önerilen yöntem, görüntü işlemede üst üste binen, kısmen gizlenen veya tüm ayrıntıların açıkça görülemediği nesneleri başarıyla oluşturabilen havza algoritmasından başka bir şey değildir. Alanda yapılan araştırmalar, testler ve uygulamalar bunun için iki önemli koşulun oluşması gerektiğini ortaya koymaktadır. Bunlardan ilki denizanasının yoğunluğunun doğru olarak elde edilmesi, diğeri ise işlemin İHA sisteminde gerçek zamanlı olarak gerçekleştirilmesidir. Bu amaçla önerilen sistem ilk olarak CPU tabanlı bir bilgisayarda test edilip doğrulanmış, ardından aynı kodun GPU tabanlı gömülü bir sisteme taşınma süreci analiz edilmiştir. Son aşamada CPU ve GPU ile elde edilen veriler karşılaştırıldığında, önerilen yöntemin bir İHA sisteminde rahatlıkla kullanılabileceği ve iklim değişikliğinin neden olduğu deniz ekosisteminin izlenmesinde başarılı sonuçlar vereceği açıktır.

Keywords

Havza algoritması Deniz anası tespiti bilgisayarlı görme gömülü sistemler uçta hesaplama

References

  • [1] Vandendriessche, S., Vansteenbrugge, L., Derweduwen, J., Maelfait, H., & Hostens, K. (2016). Jellyfish jelly press and jelly perception. Journal of Coastal Conservation, 20(2), 117-125.
  • [2] Baliarsingh, S. K., Lotliker, A. A., Srichandan, S., Samanta, A., Kumar, N., & Nair, T. B. (2020). A review of jellyfish aggregations, focusing on India’s coastal waters. Ecological Processes, 9, 1-9.
  • [3] Quiñones, J., Mianzan, H., P. S., Robinson, K. L., Adams, G. D., & Marcelo Acha, E. (2015). Climate-driven population size fluctuations of jellyfish (Chrysaora plocamia) off Peru. Marine biology, 162, 2339-2350.
  • [4] Richardson, A., Bakun, A., Hays, G., & Gibbons, M. (2009). The jellyfish joyride: causes, consequences and management responses to a more gelatinous future. Trends in ecology & evolution, 312-322.
  • [5] Sweetman, A., & Chapman, A. (2011). First observations of jelly-falls at the seafloor in a deep-sea fjord. Deep Sea Research Part I: Oceanographic Research Papers, 1206-1211.
  • [6] Ghermandi, A., Galil, B., Gowdy, J., & Nunes, P. (2015). Jellyfish outbreak impacts on recreation in the Mediterranean Sea: welfare estimates from a socioeconomic pilot survey in Israel. Ecosystem services, 140- 147.
  • [7] Sahu, B., Baliarsingh, S., Samanta, A., Srichandan, S., & Singh, S. (2020). Mass beach stranding of blue button jellies (Porpita porpita, Linnaeus, 1758) along Odisha coast during summer season. Indian J Geo-Mar Sci, 49, 1093-1096.
  • [8] Bosch-Belmar, M., Azzurro, E., Pulis, K., Milisenda, G., Fuentes, V., Yahia, O. K., . . . Piraino, S. (2017). Jellyfish blooms perception in Mediterranean finfish aquaculture. Marine Policy, 76, 1-7.
  • [9] Han, Y., Chang, Q., Ding, S., Gao, M., Zhang, B., & Li, S. (2022). Research on multiple jellyfish classification and detection based on deep learning. Multimedia Tools and Applications, 1-16.
  • [10] Martin-Abadal, M., Ruiz-Frau, A., Hinz, H., & Gonzalez-Cid, Y. (2020). Jellytoring: real-time jellyfish monitoring based on deep learning object detection. Sensors, 20(6), 1708.
  • [11] Mcilwaine, B., & Casado, M. R. (2021). JellyNet: The convolutional neural network jellyfish bloom detector. International Journal of Applied Earth Observation and Geoinformation, 97, 102279.
  • [12] Gao, M., Bai, Y., Li, Z., Li, S., Zhang, B., & Chang, Q. (2021). Real-time jellyfish classification and detection based on improved YOLOV3 algorithm. Sensors, 21(23), 8160.
  • [13] Zhang, W., Rui, F., Xiao, C., Li, H., & Li, Y. (2023). JF-YOLO: the jellyfish bloom detector based on deep learning. Multimedia Tools and Applications, 1-21.
  • [14] Gomez, C., & Purdie, H. (2016). UAV-based photogrammetry and geocomputing for hazards and disaster risk monitoring–a review. Geoenvironmental Disasters, 3, 1-11.
  • [15] Ke, Y., Wang, K., & Chen, B. (2018). Design and implementation of a hybrid UAV with model-based flight capabilities. IEEE/ASME Transactions on Mechatronics, 23, 1114-1125.
  • [16] Campion, M., Ranganathan, P., & Faruque, S. (2018). UAV swarm communication and control architectures: a review. Journal of Unmanned Vehicle Systems, 7, 93-106.
  • [17] Gnemmi, P., Changey, S., Wey, P., Roussel, E., Rey, C., Boutayeb, M., & Lozano, R. (2017). Flight phases with tests of a projectile-drone hybrid system. EEE Transactions on Control Systems Technology, 26, 2091-2105.
  • [18] Zhou, J., Mou, H., Zhou, J., Ali, M. L., Ye, H., Chen, P., & Nguyen, H. T. (2021). Qualification of soybean responses to flooding stress using UAV-based imagery and deep learning. Plant Phenomics, 1-13. doi:10.34133/2021/9892570
  • [19] Zhou, J., Zhou, J., Ye, H., Ali, M. L., Nguyen, H. T., & Chen, P. (2020). Classification of soybean leaf wilting due to drought stress using UAV-based imagery. Computers and Electronics in Agriculture, 175, 1-9.
  • [20] Feng, L., Zhang, Z., Ma, Y., Du, Q., Williams, P., Drewry, J., & Luck, B. (2020). Alfalfa yield prediction using UAVbased hyperspectral imagery and ensemble learning. Remote Sensing, 12, 1-24.
  • [21] Nevavuori, P., Narra, N., Linna, P., & Lipping, T. (2020). Crop yield prediction using multitemporal UAV data and spatio-temporal deep learning models. Remote Sensing, 12, 1-18.
  • [22] Zhao, F., Wu, X., & Wang, S. (2020). Object-oriented vegetation classification method based on UAV and satellite image fusion. Procedia Computer Science, 174, 609-615.
  • [23] Feng, Q., Liu, J., & Gong, J. (2015). UAV remote sensing for urban vegetation mapping using random forest and texture analysis. Remote sensing, 7, 1074-1094.
  • [24] Park, S., & Choi, Y. (2020). Applications of unmanned aerial vehicles in mining from exploration to reclamation: A review. Minerals, 10, 663.
  • [25] Johansen, K., Erskine, P. D., & McCabe, M. F. (2019). Using Unmanned Aerial Vehicles to assess the rehabilitation performance of open cut coal mines. Journal of cleaner production, 209, 819-833.
  • [26] Li, H., Savkin, A. V., & Vucetic, B. (2020). Autonomous area exploration and mapping in underground mine environments by unmanned aerial vehicles. Robotica, 38, 442-456.
  • [27] Lee, S., & Choi, Y. (2016). Reviews of unmanned aerial vehicle (drone) technology trends and its applications in the mining industry. Geosystem Engineering, 19, 197-204.
  • [28] Zhang, H., Tang, Z., Xie, Y., Gao, X., & Chen, Q. (2019). A watershed segmentation algorithm based on an optimal marker for bubble size measurement. Measurement, 138, 182-193.
  • [29] Zhou, J., & Yang, M. (2022). Bone Region Segmentation in Medical Images Based on Improved Watershed Algorithm. Computational Intelligence and Neuroscience. doi:10.1155/2022/3975853.
  • [30] Shi, Y., Yang, K., Jiang, T., Zhang, J., & Letaief, K. B. (2020). Communication-efficient edge AI: Algorithms and systems. IEEE Communications Surveys & Tutorials, 22, 2167-2191.
  • [31] Georgis, G., Lentaris, G., & Reisis, D. (2019). Acceleration techniques and evaluation on multi-core CPU, GPU and FPGA for image processing and super-resolution. Journal of real-time image processing, 1207-1234.
  • [32] Raschka, S., Patterson, J., & Nolet, C. (2020). Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence. Information, 11.
  • [33] Dereli, S. (2021). Micro-sized parallel system design proposal for the solution of robotics based engineering problem. Microsystem Technologies, 27, 4217-4226.
  • [34] Kalaiselvi, T., Sriramakrishnan, P., & Somasundaram, K. (2017). Survey of using GPU CUDA programming model in medical image analysis. Informatics in Medicine Unlocked, 9, 133-144.
  • [35] HajiRassouliha, A., Taberner, A. J., Nash, M. P., & Nielsen, P. M. (2018). Suitability of recent hardware accelerators (DSPs, FPGAs, and GPUs) for computer vision and image processing algorithms. Signal Processing: Image Communication, 68, 101-119.
  • [36] Rausch, T., Rashed, A., & Dustdar, S. (2021). Optimized container scheduling for data-intensive serverless edge computing. Future Generation Computer Systems, 114, 259-271. [37] Naz, N., Haseeb Malik, A., Khurshid, A. B., Aziz, F., Alouffi, B., Uddin, M. I., & AlGhamdi, A. (2020). Efficient processing of image processing applications on CPU/GPU. Mathematical Problems in Engineering, 1-14.
  • [38] Hangün, B., & Eyecioğlu, Ö. (2017). Performance comparison between OpenCV built in CPU and GPU functions on image processing operations. International Journal of Engineering Science and Application, 1, 34-41.
  • [39] Fan, B., Li, Y., Zhang, R., & Fu, Q. (2020). Review on the technological development and application of UAV systems. Chinese Journal of Electronics, 29, 199-207.

A conceptual system proposal for real-time detection of jellyfish density in coastal areas from UAV images

Year 2023, Volume: 39 Issue: 2, 192 - 203, 31.08.2023

Serkan Dereli Mehmet Okuyar Emin Güney

Abstract

In this study, a system that can be used in unmanned aerial vehicles is proposed in order to be informed early about the jellyfish infestation in coastal areas due to global warming and to take necessary precautions. The main thing in this research is that the dexterity of unmanned systems in the field of monitoring and information gathering was used to detect jellyfish infestations. The proposed method for this is nothing but the watershed algorithm, which can successfully create objects that are overlapped, partially hidden or not all details are clearly visible in image processing. Researches, tests and applications in the field reveal that two important conditions must occur for this. The first of these is to obtain the density of jellyfish correctly, and the other is to perform the process in real time in the UAV system. For this purpose, the proposed system was first tested and verified on a CPU-based computer, and then the process of moving the same code to a GPU-based embedded system was analyzed. When the data obtained with the CPU and GPU are compared at the last stage, it is obvious that the proposed method will be used easily in a UAV system and will yield successful results in monitoring the marine ecosystem caused by climate change.

Keywords

watershed algorithm jellyfish detection computer vision embedded systems edge computing

References

  • [1] Vandendriessche, S., Vansteenbrugge, L., Derweduwen, J., Maelfait, H., & Hostens, K. (2016). Jellyfish jelly press and jelly perception. Journal of Coastal Conservation, 20(2), 117-125.
  • [2] Baliarsingh, S. K., Lotliker, A. A., Srichandan, S., Samanta, A., Kumar, N., & Nair, T. B. (2020). A review of jellyfish aggregations, focusing on India’s coastal waters. Ecological Processes, 9, 1-9.
  • [3] Quiñones, J., Mianzan, H., P. S., Robinson, K. L., Adams, G. D., & Marcelo Acha, E. (2015). Climate-driven population size fluctuations of jellyfish (Chrysaora plocamia) off Peru. Marine biology, 162, 2339-2350.
  • [4] Richardson, A., Bakun, A., Hays, G., & Gibbons, M. (2009). The jellyfish joyride: causes, consequences and management responses to a more gelatinous future. Trends in ecology & evolution, 312-322.
  • [5] Sweetman, A., & Chapman, A. (2011). First observations of jelly-falls at the seafloor in a deep-sea fjord. Deep Sea Research Part I: Oceanographic Research Papers, 1206-1211.
  • [6] Ghermandi, A., Galil, B., Gowdy, J., & Nunes, P. (2015). Jellyfish outbreak impacts on recreation in the Mediterranean Sea: welfare estimates from a socioeconomic pilot survey in Israel. Ecosystem services, 140- 147.
  • [7] Sahu, B., Baliarsingh, S., Samanta, A., Srichandan, S., & Singh, S. (2020). Mass beach stranding of blue button jellies (Porpita porpita, Linnaeus, 1758) along Odisha coast during summer season. Indian J Geo-Mar Sci, 49, 1093-1096.
  • [8] Bosch-Belmar, M., Azzurro, E., Pulis, K., Milisenda, G., Fuentes, V., Yahia, O. K., . . . Piraino, S. (2017). Jellyfish blooms perception in Mediterranean finfish aquaculture. Marine Policy, 76, 1-7.
  • [9] Han, Y., Chang, Q., Ding, S., Gao, M., Zhang, B., & Li, S. (2022). Research on multiple jellyfish classification and detection based on deep learning. Multimedia Tools and Applications, 1-16.
  • [10] Martin-Abadal, M., Ruiz-Frau, A., Hinz, H., & Gonzalez-Cid, Y. (2020). Jellytoring: real-time jellyfish monitoring based on deep learning object detection. Sensors, 20(6), 1708.
  • [11] Mcilwaine, B., & Casado, M. R. (2021). JellyNet: The convolutional neural network jellyfish bloom detector. International Journal of Applied Earth Observation and Geoinformation, 97, 102279.
  • [12] Gao, M., Bai, Y., Li, Z., Li, S., Zhang, B., & Chang, Q. (2021). Real-time jellyfish classification and detection based on improved YOLOV3 algorithm. Sensors, 21(23), 8160.
  • [13] Zhang, W., Rui, F., Xiao, C., Li, H., & Li, Y. (2023). JF-YOLO: the jellyfish bloom detector based on deep learning. Multimedia Tools and Applications, 1-21.
  • [14] Gomez, C., & Purdie, H. (2016). UAV-based photogrammetry and geocomputing for hazards and disaster risk monitoring–a review. Geoenvironmental Disasters, 3, 1-11.
  • [15] Ke, Y., Wang, K., & Chen, B. (2018). Design and implementation of a hybrid UAV with model-based flight capabilities. IEEE/ASME Transactions on Mechatronics, 23, 1114-1125.
  • [16] Campion, M., Ranganathan, P., & Faruque, S. (2018). UAV swarm communication and control architectures: a review. Journal of Unmanned Vehicle Systems, 7, 93-106.
  • [17] Gnemmi, P., Changey, S., Wey, P., Roussel, E., Rey, C., Boutayeb, M., & Lozano, R. (2017). Flight phases with tests of a projectile-drone hybrid system. EEE Transactions on Control Systems Technology, 26, 2091-2105.
  • [18] Zhou, J., Mou, H., Zhou, J., Ali, M. L., Ye, H., Chen, P., & Nguyen, H. T. (2021). Qualification of soybean responses to flooding stress using UAV-based imagery and deep learning. Plant Phenomics, 1-13. doi:10.34133/2021/9892570
  • [19] Zhou, J., Zhou, J., Ye, H., Ali, M. L., Nguyen, H. T., & Chen, P. (2020). Classification of soybean leaf wilting due to drought stress using UAV-based imagery. Computers and Electronics in Agriculture, 175, 1-9.
  • [20] Feng, L., Zhang, Z., Ma, Y., Du, Q., Williams, P., Drewry, J., & Luck, B. (2020). Alfalfa yield prediction using UAVbased hyperspectral imagery and ensemble learning. Remote Sensing, 12, 1-24.
  • [21] Nevavuori, P., Narra, N., Linna, P., & Lipping, T. (2020). Crop yield prediction using multitemporal UAV data and spatio-temporal deep learning models. Remote Sensing, 12, 1-18.
  • [22] Zhao, F., Wu, X., & Wang, S. (2020). Object-oriented vegetation classification method based on UAV and satellite image fusion. Procedia Computer Science, 174, 609-615.
  • [23] Feng, Q., Liu, J., & Gong, J. (2015). UAV remote sensing for urban vegetation mapping using random forest and texture analysis. Remote sensing, 7, 1074-1094.
  • [24] Park, S., & Choi, Y. (2020). Applications of unmanned aerial vehicles in mining from exploration to reclamation: A review. Minerals, 10, 663.
  • [25] Johansen, K., Erskine, P. D., & McCabe, M. F. (2019). Using Unmanned Aerial Vehicles to assess the rehabilitation performance of open cut coal mines. Journal of cleaner production, 209, 819-833.
  • [26] Li, H., Savkin, A. V., & Vucetic, B. (2020). Autonomous area exploration and mapping in underground mine environments by unmanned aerial vehicles. Robotica, 38, 442-456.
  • [27] Lee, S., & Choi, Y. (2016). Reviews of unmanned aerial vehicle (drone) technology trends and its applications in the mining industry. Geosystem Engineering, 19, 197-204.
  • [28] Zhang, H., Tang, Z., Xie, Y., Gao, X., & Chen, Q. (2019). A watershed segmentation algorithm based on an optimal marker for bubble size measurement. Measurement, 138, 182-193.
  • [29] Zhou, J., & Yang, M. (2022). Bone Region Segmentation in Medical Images Based on Improved Watershed Algorithm. Computational Intelligence and Neuroscience. doi:10.1155/2022/3975853.
  • [30] Shi, Y., Yang, K., Jiang, T., Zhang, J., & Letaief, K. B. (2020). Communication-efficient edge AI: Algorithms and systems. IEEE Communications Surveys & Tutorials, 22, 2167-2191.
  • [31] Georgis, G., Lentaris, G., & Reisis, D. (2019). Acceleration techniques and evaluation on multi-core CPU, GPU and FPGA for image processing and super-resolution. Journal of real-time image processing, 1207-1234.
  • [32] Raschka, S., Patterson, J., & Nolet, C. (2020). Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence. Information, 11.
  • [33] Dereli, S. (2021). Micro-sized parallel system design proposal for the solution of robotics based engineering problem. Microsystem Technologies, 27, 4217-4226.
  • [34] Kalaiselvi, T., Sriramakrishnan, P., & Somasundaram, K. (2017). Survey of using GPU CUDA programming model in medical image analysis. Informatics in Medicine Unlocked, 9, 133-144.
  • [35] HajiRassouliha, A., Taberner, A. J., Nash, M. P., & Nielsen, P. M. (2018). Suitability of recent hardware accelerators (DSPs, FPGAs, and GPUs) for computer vision and image processing algorithms. Signal Processing: Image Communication, 68, 101-119.
  • [36] Rausch, T., Rashed, A., & Dustdar, S. (2021). Optimized container scheduling for data-intensive serverless edge computing. Future Generation Computer Systems, 114, 259-271. [37] Naz, N., Haseeb Malik, A., Khurshid, A. B., Aziz, F., Alouffi, B., Uddin, M. I., & AlGhamdi, A. (2020). Efficient processing of image processing applications on CPU/GPU. Mathematical Problems in Engineering, 1-14.
  • [38] Hangün, B., & Eyecioğlu, Ö. (2017). Performance comparison between OpenCV built in CPU and GPU functions on image processing operations. International Journal of Engineering Science and Application, 1, 34-41.
  • [39] Fan, B., Li, Y., Zhang, R., & Fu, Q. (2020). Review on the technological development and application of UAV systems. Chinese Journal of Electronics, 29, 199-207.
There are 38 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Serkan Dereli 0000-0002-1856-6083

Mehmet Okuyar

Emin Güney 0000-0003-0098-9018

Publication Date August 31, 2023
Published in Issue Year 2023 Volume: 39 Issue: 2

Cite

APA Dereli, S., Okuyar, M., & Güney, E. (2023). A conceptual system proposal for real-time detection of jellyfish density in coastal areas from UAV images. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, 39(2), 192-203.
AMA Dereli S, Okuyar M, Güney E. A conceptual system proposal for real-time detection of jellyfish density in coastal areas from UAV images. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi. August 2023;39(2):192-203.
Chicago Dereli, Serkan, Mehmet Okuyar, and Emin Güney. “A Conceptual System Proposal for Real-Time Detection of Jellyfish Density in Coastal Areas from UAV Images”. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 39, no. 2 (August 2023): 192-203.
EndNote Dereli S, Okuyar M, Güney E (August 1, 2023) A conceptual system proposal for real-time detection of jellyfish density in coastal areas from UAV images. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 39 2 192–203.
IEEE S. Dereli, M. Okuyar, and E. Güney, “A conceptual system proposal for real-time detection of jellyfish density in coastal areas from UAV images”, Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, vol. 39, no. 2, pp. 192–203, 2023.
ISNAD Dereli, Serkan et al. “A Conceptual System Proposal for Real-Time Detection of Jellyfish Density in Coastal Areas from UAV Images”. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 39/2 (August 2023), 192-203.
JAMA Dereli S, Okuyar M, Güney E. A conceptual system proposal for real-time detection of jellyfish density in coastal areas from UAV images. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi. 2023;39:192–203.
MLA Dereli, Serkan et al. “A Conceptual System Proposal for Real-Time Detection of Jellyfish Density in Coastal Areas from UAV Images”. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, vol. 39, no. 2, 2023, pp. 192-03.
Vancouver Dereli S, Okuyar M, Güney E. A conceptual system proposal for real-time detection of jellyfish density in coastal areas from UAV images. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi. 2023;39(2):192-203.
 Download Cover Image

✯ Etik kurul izni gerektiren, tüm bilim dallarında yapılan araştırmalar için etik kurul onayı alınmış olmalı, bu onay makalede belirtilmeli ve belgelendirilmelidir.
✯ Etik kurul izni gerektiren araştırmalarda, izinle ilgili bilgilere (kurul adı, tarih ve sayı no) yöntem bölümünde, ayrıca makalenin ilk/son sayfalarından birinde; olgu sunumlarında, bilgilendirilmiş gönüllü olur/onam formunun imzalatıldığına dair bilgiye makalede yer verilmelidir.
✯ Dergi web sayfasında, makalelerde Araştırma ve Yayın Etiğine uyulduğuna dair ifadeye yer verilmelidir.
✯ Dergi web sayfasında, hakem, yazar ve editör için ayrı başlıklar altında etik kurallarla ilgili bilgi verilmelidir.
✯ Dergide ve/veya web sayfasında, ulusal ve uluslararası standartlara atıf yaparak, dergide ve/veya web sayfasında etik ilkeler ayrı başlık altında belirtilmelidir. Örneğin; dergilere gönderilen bilimsel yazılarda, ICMJE (International Committee of Medical Journal Editors) tavsiyeleri ile COPE (Committee on Publication Ethics)’un Editör ve Yazarlar için Uluslararası Standartları dikkate alınmalıdır.
✯ Kullanılan fikir ve sanat eserleri için telif hakları düzenlemelerine riayet edilmesi gerekmektedir.