Abstract
The world is undergoing a deep and vast digital transformation and every day millions of images are produced and shared. There is an urgent need to extract valuable information from these images and use it for various applications. Object detection is at the forefront of it and there are many methods/algorithms which can be used for it. As a subfield of computer vision, object detection has been going through fast-paced changes. It is regarded as an exceptionally complex topic in the field of computer vision as it is the amalgamation of object classification as well as object localization. In this paper, we have taken an in-depth look at some of the widely varied state-of-the-art methods of object detection such as RetinaNet, ResNet, and ConvNet. These networks can be differentiated based on a variety of different features such as loss functions, data augmentation and feature extraction. We have compared them with their baseline models and analysed them to identify the most accurate methods. In our literature survey, we have studied the best in class techniques and have presented a brief overview of the current situation of object detection.
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Tinwala, W., Rauniyar, S., Agrawal, S. (2023). Survey on Convolutional Neural Networks-Based Object Detection Methods. In: Choudrie, J., Mahalle, P., Perumal, T., Joshi, A. (eds) IOT with Smart Systems. Smart Innovation, Systems and Technologies, vol 312. Springer, Singapore. https://doi.org/10.1007/978-981-19-3575-6_56
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DOI: https://doi.org/10.1007/978-981-19-3575-6_56
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