Abstract
Computer vision is the field of science that studies how computers and software can recognise and understand images and scenes. Computer vision has many different parts, such as recognising images, finding objects, making images, making images bigger, and more. Object detection in real time is a big, busy, and hard area of computer vision. Object detection is when more than one object needs to be found in an image. Image localization is when there is only one object to find in an image. This finds the objects of a class that make sense in digital pictures and videos. Real-time object detection can be used for many things, such as tracking objects, video surveillance, recognising pedestrians, counting people, self-driving cars, recognising faces, following a ball in sports, and many more. As deep learning grows quickly, more powerful tools that can learn semantic, high-level, and deeper features are made available to solve problems in traditional architectures. The network architecture, training strategy, and optimization function of these models are all different. Convolution neural network is an example of a deep learning tool that can be used to find objects with OpenCV, which is a library of programming functions that are mostly used for real-time computer vision. Most accidents happen because of things that get in the way, like other people, cars, people, fire hydrants, traffic signs, and so on. This is mostly about finding these kinds of problems that can lead to disaster.
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References
Bourdev LD, Malik J (2009) Poselets: body part detectors trained using 3D human pose annotations. IEEE 12th international conference on computer vision, ICCV 2009, Kyoto, Japan, September 27-October 4, 2009. IEEE, Kyoto, pp 1365–1372
Cadena C, Dick A, Reid I (2015) A fast, modular scene understanding sys-tem using context-aware object detection. In: 2015 IEEE international conference on robotics and automation (ICRA), Seattle, WA
Correa M, Hermosilla G, Verschae R, Ruiz-del-Solar J (2012) Humandetection and identification by robots using thermal and visual information indomestic environments. J Intell Robot Syst 66:223–243. https://doi.org/10.1007/s10846-011-9612-2
Dalal N, Triggs B (2005) Histograms of oriented gradients for humandetection. In: IEEE computer society conference on computer vision and pattern recognition. CVPR 2005, vol 1, pp 886–893. IEEE, San Diego, CA. https://doi.org/10.1109/CVPR.2005.177
Erhan D, Szegedy C, Toshev A, Anguelov D (2014) Scalable object detec-tion using deep neural networks. In: Computer vision and pattern recognition frontiers in robotics and AI. www.frontiersin.org
Shrivastava A, Gupta A, Girshick R (2016) Training region-based object detectors with online hard example mining. CVPR
Lin T-Y, Dollar P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. CVPR
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE computer society conference on computer vision and pattern recognition. CVPR 2005, vol 1, pp 886–893. IEEE, San Diego, CA. https://doi.org/10.1109/CVPR.2005.177
Erhan D, Szegedy C, Toshev A, Anguelov D (2014) Scalable object detection using deep neural networks. In: Computer vision and pattern recognition frontiers in robotics and AI. www.frontiersin.org
Girshick R, He K, Zhang X, Ren S, Sun J (2016) Fast R-CNN. ICCV 2015. Deep residual learning for image recognition. CVPR
Srinivasa Rao D, Babu ChR, Sravan Kiran V, Rajasekhar N, Srinivas K et al (2022) Plant disease classification using deep bilinear cnn. Intell Autom Soft Comput 31(1):161–176
Babu CR, Rao DS (2017) Comparison of discrete wavelet transform (DWT), discrete cosine transform (DCT) and stationary wavelet transform (SWT) based satellite image fusion techniques. Int J Cur Res Rev 9:49
Srinivasa Rao D, Seetha M, Hazarath M (2012) Iterative image fusion using neuro fuzzy logic and applications. In: International conference on machine vision and image processing (MVIP), pp 121–124
Rajesh Kumar G, Gugulothu N, Nimmala M (2019) An evolutionary feature clustering approach for anomaly detection using improved fuzzy membership function: feature clustering approach for anomaly detection. Int J Inform Technol Web Eng (IJITWE) IGI Glob 14(4):19–49
Radhakrishna V, Rajesh Kumar G, Aljawarneh S (2015) Strategic application of software process model to optimize business intelligence results. Proceedings of the the international conference on engineering & MIS 2015:1–6
Mangathayaru N, Rajesh Kumar G, Narsimha G (2016) Text mining based approach for intrusion detection. In: International conference on engineering & MIS (ICEMIS), pp 1–5. IEEE
Aljawarneh S, Radhakrishna V, Rajesh Kumar G (2019) A recent survey on challenges in security and privacy in internet of things. In: Proceedings of the 5th international conference on engineering and MIS, pp 1–9
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Rajesh Kumar, G. et al. (2023). Object Detection Using Deep Learning Approaches. In: Seetha, M., Peddoju, S.K., Pendyala, V., Chakravarthy, V.V.S.S.S. (eds) Intelligent Computing and Communication. ICICC 2022. Advances in Intelligent Systems and Computing, vol 1447. Springer, Singapore. https://doi.org/10.1007/978-981-99-1588-0_63
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