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Article

Enhanced Single Shot Small Object Detector for Aerial Imagery Using Super-Resolution, Feature Fusion and Deconvolution

1
Faculty of Science and Engineering, Anglia Ruskin University, Cambridge CB1 1PT, UK
2
The Robot Vision Team, Kingston University London, London KT1 2EE, UK
3
Department of Computer Science, Durham University, Upper MountJoy, Durham DH1 3LE, UK
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Jaroslaw Pytka, Andrzej Łukaszewicz, Zbigniew Kulesza, Wojciech Giernacki, Andriy Holovatyy and Petros Daras
Sensors 2022, 22(12), 4339; https://doi.org/10.3390/s22124339
Received: 21 March 2022 / Revised: 22 May 2022 / Accepted: 27 May 2022 / Published: 8 June 2022
One common issue of object detection in aerial imagery is the small size of objects in proportion to the overall image size. This is mainly caused by high camera altitude and wide-angle lenses that are commonly used in drones aimed to maximize the coverage. State-of-the-art general purpose object detector tend to under-perform and struggle with small object detection due to loss of spatial features and weak feature representation of the small objects and sheer imbalance between objects and the background. This paper aims to address small object detection in aerial imagery by offering a Convolutional Neural Network (CNN) model that utilizes the Single Shot multi-box Detector (SSD) as the baseline network and extends its small object detection performance with feature enhancement modules including super-resolution, deconvolution and feature fusion. These modules are collectively aimed at improving the feature representation of small objects at the prediction layer. The performance of the proposed model is evaluated using three datasets including two aerial images datasets that mainly consist of small objects. The proposed model is compared with the state-of-the-art small object detectors. Experiment results demonstrate improvements in the mean Absolute Precision (mAP) and Recall values in comparison to the state-of-the-art small object detectors that investigated in this study. View Full-Text
Keywords: deconvolution; feature fusion; small object detection; SSD; super-resolution deconvolution; feature fusion; small object detection; SSD; super-resolution
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MDPI and ACS Style

Maktab Dar Oghaz, M.; Razaak, M.; Remagnino, P. Enhanced Single Shot Small Object Detector for Aerial Imagery Using Super-Resolution, Feature Fusion and Deconvolution. Sensors 2022, 22, 4339. https://doi.org/10.3390/s22124339

AMA Style

Maktab Dar Oghaz M, Razaak M, Remagnino P. Enhanced Single Shot Small Object Detector for Aerial Imagery Using Super-Resolution, Feature Fusion and Deconvolution. Sensors. 2022; 22(12):4339. https://doi.org/10.3390/s22124339

Chicago/Turabian Style

Maktab Dar Oghaz, Mahdi, Manzoor Razaak, and Paolo Remagnino. 2022. "Enhanced Single Shot Small Object Detector for Aerial Imagery Using Super-Resolution, Feature Fusion and Deconvolution" Sensors 22, no. 12: 4339. https://doi.org/10.3390/s22124339

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