Software Engineering Perspective on Object Detection Studies: Current Status and Challenges
Abstract views: 23
/ 
PDF downloads: 8
Keywords:
Software Engineering, Object Detection, Deep Learning, Software, Artificial IntelligenceAbstract
Software is a popular field of study where studies in this field increase as its use increases.
Software engineering is a branch of engineering that covers the development and maintenance processes
of software products of different sizes and types. Software engineering is a field where studies are carried
out in many different fields. In recent years, it has also shown a great change with its current work areas.
With the development of artificial intelligence and deep learning studies, the work areas of software
engineers are also updated and developed. Object detection and recognition applications are software that
are intensively studied and are the basic parts of many software products. Performing object detection and
recognition in a video sequence or image is considered a difficult process. However, in recent years,
software engineers have achieved quite successful results in object detection applications. While studies
in this field continue, this study was presented in order to reveal the current situation. A study was
conducted to reveal the difficulties, new trends and expectations of software engineers who will consider
working in the field of object detection.
Downloads
References
Storey, M.-A., Ernst, N.A., Williams, C., Kalliamvakou, E.: The who, what, how of software engineering research: a socio-technical framework. Empir. Softw. Eng. 25, 4097–4129 (2020)
Wang, Z., Guo, J., Bu, D., & Shi, C. (2023). Investigating failure patterns in machine learning-based object detection tasks in Software Development Courses. Journal of Internet Technology, 24(4), 1001-1008.
Krüger, J., Li, Y., Zhu, C., Chechik, M., Berger, T., & Rubin, J. (2023, November). A vision on intentions in software engineering. In Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (pp. 2117-2121).
Lenberg, P., Feldt, R., Gren, L., Wallgren Tengberg, L. G., Tidefors, I., & Graziotin, D. (2024). Qualitative software engineering research: Reflections and guidelines. Journal of Software: Evolution and Process, 36(6), e2607.
Crawford, T., Duong, S., Fueston, R., Lawani, A., Owoade, S., Uzoka, A., ... & Yazdinejad, A. (2023). Ai in software engineering: A survey on project management applications. arXiv preprint arXiv:2307.15224.
Tan, F. G., Yüksel, A. S., Aydemir, E., & Ersoy, M. (2021). Derin Öğrenme Teknikleri ile Nesne Tespiti ve Takibi Üzerine Bir İnceleme. Avrupa Bilim ve Teknoloji Dergisi, (25), 159-171.
Aktürk, S., & Serbest, K. (2022). Nesne Tespiti İçin Derin Öğrenme Kütüphanelerinin İncelenmesi. Journal of Smart Systems Research, 3(2), 97-119
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2014.81
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2016.91
Zhao, Z. Q., Zheng, P., Xu, S. T., & Wu, X. (2019). Object Detection with Deep Learning: A Review. IEEE Transactions on Neural Networks and Learning Systems, 30(11), 3212–3232. https://doi.org/10.1109/TNNLS.2018.2876865
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., & Fei-Fei, L. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115(3), 211–252.
