Multi-Vision Network for Accurate and Real-Time Small Object Detection in Optical Remote Sensing Images

Han, Wenxuan; Kuerban, Alifu; Yang, Yuchun; Huang, Zitong; Liu, Binghui; Gao, Jie

doi:10.1109/lgrs.2020.3044422

Cited by 16 publications

(8 citation statements)

References 19 publications

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“…At the same time, since the features of adjacent layers have strong correlations and inheritance, fusing the features does not trigger the problem of network confusion caused by large differences in terms of information, and only a small number of additional parameters and calculations are required. [58,62,[81][82][83].…”

Section: The Multi-scale Feature Fusion Based Methodsmentioning

confidence: 99%

“…Wang et al [95] designed a receptive field module with five branches, in which four layers carry out dilated convolution to extract local context information while the other branches perform global pooling to extract global context information in order to obtain the discriminative features. Han et al [58] inserted three serial dilated convolution layers into the residual module to extract context information. Yuan et al [87] designed four parallel dilated convolution branches to increase the receptive field and help the network generate higher-resolution feature maps for local context information.…”

Section: The Mining Context Information-based Methodsmentioning

confidence: 99%

“…Blurred transformation [58], also called smoothing transformation, is a means of making images blurred. The main effect is that it reduces the otherness between pixels to smooth images and reduces the noise and the level of detail in the images to alleviate the network's reliance on image quality.…”

Section: Data Preprocessingmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey

Wang

Zhang

et al. 2022

Remote Sensing

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Object detection in remote sensing images (RSIs) requires the locating and classifying of objects of interest, which is a hot topic in RSI analysis research. With the development of deep learning (DL) technology, which has accelerated in recent years, numerous intelligent and efficient detection algorithms have been proposed. Meanwhile, the performance of remote sensing imaging hardware has also evolved significantly. The detection technology used with high-resolution RSIs has been pushed to unprecedented heights, making important contributions in practical applications such as urban detection, building planning, and disaster prediction. However, although some scholars have authored reviews on DL-based object detection systems, the leading DL-based object detection improvement strategies have never been summarized in detail. In this paper, we first briefly review the recent history of remote sensing object detection (RSOD) techniques, including traditional methods as well as DL-based methods. Then, we systematically summarize the procedures used in DL-based detection algorithms. Most importantly, starting from the problems of complex object features, complex background information, tedious sample annotation that will be faced by high-resolution RSI object detection, we introduce a taxonomy based on various detection methods, which focuses on summarizing and classifying the existing attention mechanisms, multi-scale feature fusion, super-resolution and other major improvement strategies. We also introduce recognized open-source remote sensing detection benchmarks and evaluation metrics. Finally, based on the current state of the technology, we conclude by discussing the challenges and potential trends in the field of RSOD in order to provide a reference for researchers who have just entered the field.

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Section: The Multi-scale Feature Fusion Based Methodsmentioning

confidence: 99%

Section: The Mining Context Information-based Methodsmentioning

confidence: 99%

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Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey

Wang

Zhang

et al. 2022

Remote Sensing

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“…Semantic contextual features from upper and lower layers are integrated using the Semantic Contextual Fusion Module (SCFM) and delivered in layers. Another model, MVNet [91], proposes a multivisual small target detector using Multi-Scale Residual Blocks (MRBs) with extended convolution in cascaded residual blocks. This module helps capture information at different scales and spatial contexts, enabling a more comprehensive understanding of the targets and improving the detection accuracy for objects of varying sizes in remote sensing images.…”

Section: Methodsmentioning

confidence: 99%

A Survey of Small Object Detection Based on Deep Learning in Aerial Images

Wang

Chen

2023

Preprint

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Small object detection poses a formidable challenge in the field of computer vision, particularly when it comes to analyzing aerial remote sensing images. Despite the rapid development of deep learning and significant progress in detection techniques in natural scenes, the migration of these algorithms to aerial images has not met expectations. This is primarily due to limitations in imaging acquisition conditions, including small target size, viewpoint specificity, background complexity, as well as scale and orientation diversity. Although the increasing application of deep learning-based algorithms to overcome these problems, few studies have summarized the optimization of different deep learning strategies used for small target detection in aerial images. Therefore, this paper aims to explore the application of deep learning methods for small object detection in aerial images. The primary challenges in small object detection in aerial images will be summarized. Next, a meticulous analysis and categorization of the prevailing deep learning optimization strategies employed to surmount the challenges encountered in aerial image detection is undertaken. Following that, we provide a comprehensive presentation of the object detection datasets utilized in aerial remote sensing images, along with the evaluation metrics employed. Additionally, we furnish experimental data pertaining to the currently proposed detection algorithms. Finally, the advantages and disadvantages of various optimization strategies and potential development trends are discussed. Hopefully, it can provide a reference for researchers in this field.

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“…Etten et al [39] proposed the You Only Look Twice (YOLT) algorithm, which applied YOLO to the remote sensing field for the first time. Han et al [40] designed a multi-scale receptive field enhancement module (MRFEM) for small objects. Zhang et al [41] proposed a multi-stage feature enhancement pyramid network to fuse features at varying scales for small objects with blurry edges.…”

Section: Remote Sensing Images Object Detectionmentioning

confidence: 99%

High-Resolution Network with Transformer Embedding Parallel Detection for Small Object Detection in Optical Remote Sensing Images

Zhang,

Liu,

Chang

et al. 2023

Remote Sensing

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Small object detection in remote sensing enables the identification and analysis of unapparent but important information, playing a crucial role in various ground monitoring tasks. Due to the small size, the available feature information contained in small objects is very limited, making them more easily buried by the complex background. As one of the research hotspots in remote sensing, although many breakthroughs have been made, there still exist two significant shortcomings for the existing approaches: first, the down-sampling operation commonly used for feature extraction can barely preserve weak features of objects in a tiny size; second, the convolutional neural network methods have limitations in modeling global context to address cluttered backgrounds. To tackle these issues, a high-resolution network with transformer embedding parallel detection (HRTP-Net) is proposed in this paper. A high-resolution feature fusion network (HR-FFN) is designed to solve the first problem by maintaining high spatial resolution features with enhanced semantic information. Furthermore, a Swin-transformer-based mixed attention module (STMA) is proposed to augment the object information in the transformer block by establishing a pixel-level correlation, thereby enabling global background–object modeling, which can address the second shortcoming. Finally, a parallel detection structure for remote sensing is constructed by integrating the attentional outputs of STMA with standard convolutional features. The proposed method effectively mitigates the impact of the intricate background on small objects. The comprehensive experiment results on three representative remote sensing datasets with small objects (MASATI, VEDAI and DOTA datasets) demonstrate that the proposed HRTP-Net achieves a promising and competitive performance.

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Multi-Vision Network for Accurate and Real-Time Small Object Detection in Optical Remote Sensing Images

Cited by 16 publications

References 19 publications

Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey

Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey

A Survey of Small Object Detection Based on Deep Learning in Aerial Images

High-Resolution Network with Transformer Embedding Parallel Detection for Small Object Detection in Optical Remote Sensing Images

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