Computer-Vision-Based Statue Detection with Gaussian Smoothing Filter and EfficientDet

Mubarak Auwalu Saleh (), Zubaida Said Ameen, Chadi Altrjman and Fadi Al-Turjman
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Mubarak Auwalu Saleh: Artificial Intelligence Engineering Department, AI and Robotics Institute, Near East University, North Cyprus via Mersin 10, Nicosia 99138, Turkey
Zubaida Said Ameen: Artificial Intelligence Engineering Department, AI and Robotics Institute, Near East University, North Cyprus via Mersin 10, Nicosia 99138, Turkey
Chadi Altrjman: Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, North Cyprus via Mersin 10, Kyrenia 99320, Turkey
Fadi Al-Turjman: Artificial Intelligence Engineering Department, AI and Robotics Institute, Near East University, North Cyprus via Mersin 10, Nicosia 99138, Turkey

Sustainability, 2022, vol. 14, issue 18, 1-10

Abstract: Smart tourism is a developing industry, and numerous nations are planning to establish smart cities in which technology is employed to make life easier and link nearly everything. Many researchers have created object detectors; however, there is a demand for lightweight versions that can fit into smartphones and other edge devices. The goal of this research is to demonstrate the notion of employing a mobile application that can detect statues efficiently on mobile applications, and also improve the performance of the models by employing the Gaussian Smoothing Filter (GSF). In this study, three object detection models, EfficientDet—D0, EfficientDet—D2 and EfficientDet—D4, were trained on original and smoothened images; moreover, their performance was compared to find a model efficient detection score that is easy to run on a mobile phone. EfficientDet—D4, trained on smoothened images, achieves a Mean Average Precision (mAP) of 0.811, an mAP-50 of 1 and an mAP-75 of 0.90.

Keywords: smart cities; computer vision; object detection; mobile application (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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