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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 10, 2023.
Abstract: Object recognition has gained significance due to the rise in CCTV surveillance and the need for automated detection of objects or activities in images and videos. Lightweight process frameworks are in demand for sensor networks. While Convolutional Neural Networks (CNNs) are widely used in computer vision, many existing architectures are specialized. This paper introduces the Dimension-Based Generic Convolution Block (DBGC), enhancing CNNs with dimension-wise selection of kernels for improved performance. The DBGC offers flexibility for height, width, and depth kernels and can be applied to different dimension combinations. A key feature is the dimension selector block. Unoptimized kernel dimensions reduce computational operations and accuracy, while semi-optimized ones maintain accuracy with fewer operations. Optimized dimensions provide 5-6% higher accuracy and reduced operations. This work addresses the challenge of generic architecture in object recognition research.
Dulari Bhatt, Chirag Patel, Madhuri Chopade, Madhvi Dave and Chintan Patel, “Modified Deep Neural Network for Object Recognition” International Journal of Advanced Computer Science and Applications(IJACSA), 14(10), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0141046
@article{Bhatt2023,
title = {Modified Deep Neural Network for Object Recognition},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0141046},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0141046},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {10},
author = {Dulari Bhatt and Chirag Patel and Madhuri Chopade and Madhvi Dave and Chintan Patel}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.