Improving Object Detection using Enhanced EfficientNet Architecture

EfficientNet is designed to achieve top accuracy while utilizing fewer parameters, in addition to less computational resources compared to previous models. In this paper, we are presenting compound scaling method that re-weight the network’s width (w), depth(d), and resolution (r), which leads to better performance than traditional methods that scale only one or two of these dimensions by adjusting the hyperparameters of the model. Additionally, we are presenting an enhanced EfficientNet Backbone architecture. We show that EfficientNet achieves top accuracy on the ImageNet dataset, while being up to 8.4x smaller and up to 6.1x faster than previous top performing models. The effec- tiveness demonstrated in EfficientNet on transfer learning and object detection tasks, where it achieves higher accuracy with fewer parameters and less computation. Henceforward, the proposed enhanced architecture will be discussed in detail and compared to the original architecture. Our approach provides a scalable and efficient solution for both academic research and practical applications, where resource constraints are often a limiting factor.

Author dc:creator

• Kamel Ibrahim, Michael

Advisor dc:contributor.advisor

• El-Sharkawy, Mohamed

• Autonomous Vehicles
• EfficientNet Architecture
• Object Detection
• Convolutional Neural Networks
• Machine Learning

Language dc:language.iso

en_US

Identifier URI

https://doi.org/10.7912/1MFK-EB76

OAI identifier oai:identifier

oai:scholarworks.indianapolis.iu.edu:1805/35266