Adaptive Saliency Biased Loss for Object Detection in Aerial Images

Sun, Peng; Chen, Guang; Shang, Yi

doi:10.1109/tgrs.2020.2980023

Cited by 25 publications

(10 citation statements)

References 44 publications

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“…The effectiveness of context information has been verified by many studies [43][44][45] in aerial object detection, especially for small objects or occluded objects. The intuition behind these works is that low-level high-resolution features from shallow layers favor localization, while high-level low-resolution features from deeper layers help classification.…”

Section: Context Information In Rs Object Detectionmentioning

confidence: 84%

ℱ3-Net: Feature Fusion and Filtration Network for Object Detection in Optical Remote Sensing Images

Xiong

et al. 2020

Remote Sensing

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Object detection in remote sensing (RS) images is a challenging task due to the difficulties of small size, varied appearance, and complex background. Although a lot of methods have been developed to address this problem, many of them cannot fully exploit multilevel context information or handle cluttered background in RS images either. To this end, in this paper, we propose a feature fusion and filtration network (F3-Net) to improve object detection in RS images, which has higher capacity of combining the context information at multiple scales while suppressing the interference from the background. Specifically, F3-Net leverages a feature adaptation block with a residual structure to adjust the backbone network in an end-to-end manner, better considering the characteristics of RS images. Afterward, the network learns the context information of the object at multiple scales by hierarchically fusing the feature maps from different layers. In order to suppress the interference from cluttered background, the fused feature is then projected into a low-dimensional subspace by an additional feature filtration module. As a result, more relevant and accurate context information is extracted for further detection. Extensive experiments on DOTA, NWPU VHR-10, and UCAS AOD datasets demonstrate that the proposed detector achieves very promising detection performance.

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Section: Context Information In Rs Object Detectionmentioning

confidence: 84%

ℱ3-Net: Feature Fusion and Filtration Network for Object Detection in Optical Remote Sensing Images

Xiong

et al. 2020

Remote Sensing

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“…Based on the SSD paradigm, AF-SSD [23] improves the performance of ORSI object detection by designing exquisite enhancement modules such as the encoding-decoding module and spatial and channel attention modules. Sun et al [24] proposed an adaptive saliency-biased loss (ASBL) to train the RetinaNet and dramatically improved the performance of detection in the ORSIs. In addition, the work in [25,26] proposed the advanced object detection architecture that involves both spatial and temporal domain information in the decision.…”

Section: One-stage Object Detection Methodsmentioning

confidence: 99%

Multi-Sector Oriented Object Detector for Accurate Localization in Optical Remote Sensing Images

et al. 2021

Remote Sensing

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Oriented object detection in optical remote sensing images (ORSIs) is a challenging task since the targets in ORSIs are displayed in an arbitrarily oriented manner and on small scales, and are densely packed. Current state-of-the-art oriented object detection models used in ORSIs primarily evolved from anchor-based and direct regression-based detection paradigms. Nevertheless, they still encounter a design difficulty from handcrafted anchor definitions and learning complexities in direct localization regression. To tackle these issues, in this paper, we proposed a novel multi-sector oriented object detection framework called MSO2-Det, which quantizes the scales and orientation prediction of targets in ORSIs via an anchor-free classification-to-regression approach. Specifically, we first represented the arbitrarily oriented bounding box as four scale offsets and angles in four quadrant sectors of the corresponding Cartesian coordinate system. Then, we divided the scales and angle space into multiple discrete sectors and obtained more accurate localization information by a coarse-granularity classification to fine-grained regression strategy. In addition, to decrease the angular-sector classification loss and accelerate the network’s convergence, we designed a smooth angular-sector label (SASL) that smoothly distributes label values with a definite tolerance radius. Finally, we proposed a localization-aided detection score (LADS) to better represent the confidence of a detected box by combining the category-classification score and the sector-selection score. The proposed MSO2-Det achieves state-of-the-art results on three widely used benchmarks, including the DOTA, HRSC2016, and UCAS-AOD data sets.

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“…The accuracy level that can be obtained across different remote sensing tasks highly depends on the characteristics of the sensors, especially on their spatial resolution [34]. In this regard, sensors which provide lower resolution products are mainly used for image recognition of whole image chips [35], so-called scene labeling, whereas, sensors with a higher resolution are used for a wider range of tasks including image segmentation [36] and object detection [37].…”

Section: Deep Learning In Remote Sensing: Applications Using Sentinel-1 and Sentinel-2mentioning

confidence: 99%

A Deep Learning Approach to an Enhanced Building Footprint and Road Detection in High-Resolution Satellite Imagery

et al. 2021

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The detection of building footprints and road networks has many useful applications including the monitoring of urban development, real-time navigation, etc. Taking into account that a great deal of human attention is required by these remote sensing tasks, a lot of effort has been made to automate them. However, the vast majority of the approaches rely on very high-resolution satellite imagery (<2.5 m) whose costs are not yet affordable for maintaining up-to-date maps. Working with the limited spatial resolution provided by high-resolution satellite imagery such as Sentinel-1 and Sentinel-2 (10 m) makes it hard to detect buildings and roads, since these labels may coexist within the same pixel. This paper focuses on this problem and presents a novel methodology capable of detecting building and roads with sub-pixel width by increasing the resolution of the output masks. This methodology consists of fusing Sentinel-1 and Sentinel-2 data (at 10 m) together with OpenStreetMap to train deep learning models for building and road detection at 2.5 m. This becomes possible thanks to the usage of OpenStreetMap vector data, which can be rasterized to any desired resolution. Accordingly, a few simple yet effective modifications of the U-Net architecture are proposed to not only semantically segment the input image, but also to learn how to enhance the resolution of the output masks. As a result, generated mappings quadruplicate the input spatial resolution, closing the gap between satellite and aerial imagery for building and road detection. To properly evaluate the generalization capabilities of the proposed methodology, a data-set composed of 44 cities across the Spanish territory have been considered and divided into training and testing cities. Both quantitative and qualitative results show that high-resolution satellite imagery can be used for sub-pixel width building and road detection following the proper methodology.

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Adaptive Saliency Biased Loss for Object Detection in Aerial Images

Cited by 25 publications

References 44 publications

ℱ3-Net: Feature Fusion and Filtration Network for Object Detection in Optical Remote Sensing Images

ℱ3-Net: Feature Fusion and Filtration Network for Object Detection in Optical Remote Sensing Images

Multi-Sector Oriented Object Detector for Accurate Localization in Optical Remote Sensing Images

A Deep Learning Approach to an Enhanced Building Footprint and Road Detection in High-Resolution Satellite Imagery

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