Abstract
Object detection and classification is a very important integrant of computer vision domain. It has its role in various sectors of life as security, safety, fun, heath & comfort etc. Under safety and security, surveillance is one critical application area where, Object detection has gained the growing importance. Object in such case could be human being and other suspicious and sensitive objects. Correct detection and classification on accuracy measures is always a challenge in these problems. Now days, deep learning techniques are getting utilized as an effective and efficient tool for different classification problems. Looking over these facts, a review of available deep learning architectures has been presented in this paper, for the problem of object detection and classification. The classification models considered for review are AlexNet, VGG Net, GoogLeNet, ResNet. The dataset used for experimentation is Caltech-101 dataset and the standard performance measures utilized for evaluation are True Positive Rate (TPR), False Positive Rate (FPR) and Accuracy.
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Ansari, M.A., Singh, D.K. (2019). Review of Deep Learning Techniques for Object Detection and Classification. In: Verma, S., Tomar, R., Chaurasia, B., Singh, V., Abawajy, J. (eds) Communication, Networks and Computing. CNC 2018. Communications in Computer and Information Science, vol 839. Springer, Singapore. https://doi.org/10.1007/978-981-13-2372-0_37
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DOI: https://doi.org/10.1007/978-981-13-2372-0_37
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