TinyEmergencyNet: a hardware-friendly ultra-lightweight deep learning model for aerial scene image classification

Mogaka, Obed M.; Zewail, Rami; Inoue, Koji; Sayed, Mohammed S.

doi:10.1007/s11554-024-01430-y

J Real-Time Image Proc

2024

DOI: 10.1007/s11554-024-01430-y

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TinyEmergencyNet: a hardware-friendly ultra-lightweight deep learning model for aerial scene image classification

Obed M. Mogaka,

Rami Zewail,

Koji Inoue

et al.

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2024

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Cited by 3 publications

References 31 publications

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Natural disaster damage analysis using lightweight spatial feature aggregated deep learning model

Abraham,

Abo-Zahhad,

Abdelwahab

2024

Earth Sci Inform

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Natural disaster damage analysis using lightweight spatial feature aggregated deep learning model

Abraham,

Abo-Zahhad,

Abdelwahab

2024

Earth Sci Inform

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Prairie Dog Optimization Algorithm with deep learning assisted based Aerial Image Classification on UAV imagery

K. Alkhalifa,

Kashif Saeed,

M. Othman

et al. 2024

Heliyon

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Object/Scene Recognition Based on a Directional Pixel Voting Descriptor

Aguilar-González,

Medina Santiago,

Osuna-Coutiño

2024

Applied Sciences

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Detecting objects in images is crucial for several applications, including surveillance, autonomous navigation, augmented reality, and so on. Although AI-based approaches such as Convolutional Neural Networks (CNNs) have proven highly effective in object detection, in scenarios where the objects being recognized are unknow, it is difficult to generalize an AI model for such tasks. In another trend, feature-based approaches like SIFT, SURF, and ORB offer the capability to search any object but have limitations under complex visual variations. In this work, we introduce a novel edge-based object/scene recognition method. We propose that utilizing feature edges, instead of feature points, offers high performance under complex visual variations. Our primary contribution is a directional pixel voting descriptor based on image segments. Experimental results are promising; compared to previous approaches, ours demonstrates superior performance under complex visual variations and high processing speed.

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TinyEmergencyNet: a hardware-friendly ultra-lightweight deep learning model for aerial scene image classification

Cited by 3 publications

References 31 publications

Natural disaster damage analysis using lightweight spatial feature aggregated deep learning model

Natural disaster damage analysis using lightweight spatial feature aggregated deep learning model

Prairie Dog Optimization Algorithm with deep learning assisted based Aerial Image Classification on UAV imagery

Object/Scene Recognition Based on a Directional Pixel Voting Descriptor

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