scholarly journals Selecting Post-Processing Schemes for Accurate Detection of Small Objects in Low-Resolution Wide-Area Aerial Imagery

2022 ◽  
Vol 14 (2) ◽  
pp. 255
Author(s):  
Xin Gao ◽  
Sundaresh Ram ◽  
Rohit C. Philip ◽  
Jeffrey J. Rodríguez ◽  
Jeno Szep ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Object Detection ◽  
Aerial Imagery ◽  
Wide Area ◽  
Small Object ◽  
Post Processing ◽  
Low Resolution ◽  
Processing Scheme ◽  
Detection Algorithms

In low-resolution wide-area aerial imagery, object detection algorithms are categorized as feature extraction and machine learning approaches, where the former often requires a post-processing scheme to reduce false detections and the latter demands multi-stage learning followed by post-processing. In this paper, we present an approach on how to select post-processing schemes for aerial object detection. We evaluated combinations of each of ten vehicle detection algorithms with any of seven post-processing schemes, where the best three schemes for each algorithm were determined using average F-score metric. The performance improvement is quantified using basic information retrieval metrics as well as the classification of events, activities and relationships (CLEAR) metrics. We also implemented a two-stage learning algorithm using a hundred-layer densely connected convolutional neural network for small object detection and evaluated its degree of improvement when combined with the various post-processing schemes. The highest average F-scores after post-processing are 0.902, 0.704 and 0.891 for the Tucson, Phoenix and online VEDAI datasets, respectively. The combined results prove that our enhanced three-stage post-processing scheme achieves a mean average precision (mAP) of 63.9% for feature extraction methods and 82.8% for the machine learning approach.

scholarly journals Robust Small-Object Detection for Outdoor Wide-Area Surveillance

2008 ◽  
Vol E91-D (7) ◽  
pp. 1922-1928 ◽  
Author(s):  
D. ABE ◽  
E. SEGAWA ◽  
O. NAKAYAMA ◽  
M. SHIOHARA ◽  
S. SASAKI ◽  
...  
Keyword(s):  
Object Detection ◽  
Wide Area ◽  
Small Object ◽  
Area Surveillance ◽  
Small Object Detection ◽  
Wide Area Surveillance

scholarly journals Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

2020 ◽  
Author(s):  
Jakaria Rabbi ◽  
Nilanjan Ray ◽  
Matthias Schubert ◽  
Subir Chowdhury ◽  
Dennis Chao
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Super Resolution ◽  
Detection Performance ◽  
Superior Performance ◽  
Single Shot ◽  
Small Object ◽  
Remote Sensing Images ◽  
Low Resolution ◽  
End To End

The detection performance of small objects in remote sensing images is not satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) shows remarkable image enhancement performance, but reconstructed images miss high-frequency edge information. Therefore, object detection performance degrades for the small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN (EESRGAN) to improve the image quality of remote sensing images and used different detector networks in an end-to-end manner where detector loss is backpropagated into the EESRGAN to improve the detection performance. We propose an architecture with three components: ESRGAN, Edge Enhancement Network (EEN), and Detection network. We use residual-in-residual dense blocks (RRDB) for both the GAN and EEN, and for the detector network, we use the faster region-based convolutional network (FRCNN) (two-stage detector) and single-shot multi-box detector (SSD) (one stage detector). Extensive experiments on car overhead with context and oil and gas storage tank (created by us) data sets show superior performance of our method compared to the standalone state-of-the-art object detectors.

scholarly journals Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

2020 ◽  
Author(s):  
Jakaria Rabbi ◽  
Nilanjan Ray ◽  
Matthias Schubert ◽  
Subir Chowdhury ◽  
Dennis Chao
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Super Resolution ◽  
Detection Performance ◽  
Superior Performance ◽  
Single Shot ◽  
Small Object ◽  
Remote Sensing Images ◽  
Low Resolution ◽  
End To End

The detection performance of small objects in remote sensing images is not satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) shows remarkable image enhancement performance, but reconstructed images miss high-frequency edge information. Therefore, object detection performance degrades for small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN (EESRGAN) to improve the image quality of remote sensing images and use different detector networks in an end-to-end manner where detector loss is backpropagated into the EESRGAN to improve the detection performance. We propose an architecture with three components: ESRGAN, Edge Enhancement Network (EEN), and Detection network. We use residual-in-residual dense blocks (RRDB) for both the ESRGAN and EEN, and for the detector network, we use the faster region-based convolutional network (FRCNN) (two-stage detector) and single-shot multi-box detector (SSD) (one stage detector). Extensive experiments on a public (car overhead with context) and a self-assembled (oil and gas storage tank) satellite dataset show superior performance of our method compared to the standalone state-of-the-art object detectors.

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