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Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network (2003.09085v5)

Published 20 Mar 2020 in cs.CV and cs.LG

Abstract: 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.

Citations (209)

Summary

  • The paper presents an integrated approach that merges an edge-enhanced GAN for super-resolution with modern object detectors to improve small-object detection in low-resolution images.
  • The method utilizes residual-in-residual dense blocks and a relativistic discriminator, yielding AP improvements up to 95.5% on the COWC dataset and 83.2% on OGST.
  • The approach offers theoretical insights and practical applications for enhanced remote sensing, benefiting surveillance and monitoring tasks in diverse contexts.

An Expert Review of "Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network"

The paper presented by Rabbi et al. details an innovative approach to tackle the persistent challenge of detecting small objects in low-resolution remote sensing images. The authors propose a novel architecture integrating an Edge-Enhanced Super-Resolution GAN (EESRGAN) seamlessly with object detection networks, aiming to improve the quality of remote sensing images and subsequent detection performance.

Overview of Methodology

The core of the proposed method is an end-to-end architecture comprising a super-resolution network and an object detector. The super-resolution component includes EESRGAN, which is tasked with enhancing the resolution of input low-resolution (LR) images. The GAN architecture utilizes residual-in-residual dense blocks (RRDB), which significantly enhance network capacity without batch normalization, drawing inspiration from the ESRGAN model. The key innovation here is the edge-enhancement network (EEN), which refines edge details crucial for accurate detection. The model’s discriminator section includes the relativistic average discriminator and a detector network that aids loss backpropagation, further honing the image quality for object detection tasks.

Two detectors are utilized: the Faster R-CNN and SSD. The Faster R-CNN uses the ResNet-50-FPN as its backbone, making it highly suitable for small object detection due to its feature pyramid network capabilities. SSD is employed for its speed, offering a trade-off against some detection precision.

Numerical Outcomes and Claims

The findings indicate a significant performance enhancement in detecting small objects using the proposed method compared to using traditional detectors on original low-resolution imagery. Specifically, the end-to-end trained EESRGAN-FRCNN exhibited marked improvements in Average Precision (AP) across varied IoU thresholds, closely approaching the efficacy of detection models operating on genuine high-resolution images.

In practical terms, the paper reports that the EESRGAN-FRCNN, when trained and tested in an end-to-end manner, yields AP values reaching up to 95.5% for the Car Overhead With Context (COWC) dataset and 83.2% for the Oil and Gas Storage Tank (OGST) dataset at IoU=0.5:0.95. These results represent a substantial enhancement over baseline approaches, reflecting the architecture’s ability to effectively resolve and detect objects that were previously indistinguishable in LR images.

Practical and Theoretical Implications

The approach outlined in this paper holds both theoretical and practical significance. Theoretically, it demonstrates the potential of integrating GAN-based super-resolution techniques with object detection networks, emphasizing joint optimization for enhancing object detection accuracy in remote sensing applications. The methodology showcases how edge information, when preserved and accentuated, can play a critical role in improving object detection in complex scenarios.

Practically, the utilization of satellite imagery such as that of oil and gas storage tanks can benefit regulatory authorities, researchers, and industries that require intricate monitoring of terrain features. The promising results with datasets like COWC and OGST underline the architecture’s applicability across diverse surveillance and monitoring needs, directly addressing the cost and accuracy trade-offs associated with obtaining frequent high-resolution satellite imagery.

Prospect for Future Developments

Although the current architecture addresses several key limitations in remote sensing image analysis, future developments could focus on generalization across more varied datasets and object types. Incorporating datasets that include multiple classes and broader geographic diversity may further solidify the model’s adaptability and robustness. Additionally, leveraging advancements in GAN stability and efficiency could push the boundaries of super-resolution, potentially paving the way for real-time implementation of such architectures in broader applications in the remote sensing domain.

In conclusion, the work of Rabbi et al. signifies a step forward in the enhancement and application of deep learning techniques for remote sensing, contributing valuable insights and methodologies for the ongoing challenge of small-object detection in low-resolution satellite imagery.