Aerial Object Detection Using Deep Learning: A Review

Goyal, Vinat; Singh, Rishu; Dhawley, Mrudul; Kumar, Aveekal; Sharma, Sanjeev

doi:10.1007/978-981-19-7346-8_8

Cited by 2 publications

(2 citation statements)

References 37 publications

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“…However, remote sensing images are different from general natural images in that they have more complex spectral features, scene features, multiscale features, etc. Therefore, the application of these strongly supervised network models in object detection tasks in remote sensing images still faces many challenges [30,31]. The major challenges currently facing remote sensing object detection tasks are how to detect when there are very few samples, and how to use the trained model to adapt to infer new categories of objects.…”

Section: Introductionmentioning

confidence: 99%

Few-Shot Object Detection in Remote Sensing Imagery via Fuse Context Dependencies and Global Features

Wang

Sui

et al. 2023

Remote Sensing

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The rapid development of Earth observation technology has promoted the continuous accumulation of images in the field of remote sensing. However, a large number of remote sensing images still lack manual annotations of objects, which makes the strongly supervised deep learning object detection method not widely used, as it lacks generalization ability for unseen object categories. Considering the above problems, this study proposes a few-shot remote sensing image object detection method that integrates context dependencies and global features. The method can be used to fine-tune the model with a small number of sample annotations based on the model trained in the base class, as a way to enhance the detection capability of new object classes. The method proposed in this study consists of three main modules, namely, the meta-feature extractor (ME), reweighting module (RM), and feature fusion module (FFM). These three modules are respectively used to enhance the context dependencies of the query set features, improve the global features of the support set that contains annotations, and finally fuse the query set features and support set features. The baseline of the meta-feature extractor of the entire framework is based on the optimized YOLOv5 framework. The reweighting module of the support set feature extraction is based on a simple convolutional neural network (CNN) framework, and the foreground feature enhancement of the support sets was made in the preprocessing stage. This study achieved beneficial results in the two benchmark datasets NWPU VHR-10 and DIOR. Compared with the comparison methods, the proposed method achieved the best performance in the object detection of the base class and the novel class.

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Section: Introductionmentioning

confidence: 99%

Few-Shot Object Detection in Remote Sensing Imagery via Fuse Context Dependencies and Global Features

Wang

Sui

et al. 2023

Remote Sensing

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“…O BJECT detection is one of the fundamental tasks in the field of aerial image interpretation. It aims to accurately locate and identify objects that need to be detected in aerial images, such as pedestrians, vehicles, and aircraft, through image algorithms [1]. It is essential in various practical applications such as military reconnaissance, field search and rescue.…”

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confidence: 99%

Effective Anchor Adaptation and Feature Enhancement Strategies for Tiny Object Detection in Aerial Images

Liu,

Tong,

Miao

et al. 2024

IEEE Access

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In recent years, research based on anchor-based two-stage detectors has achieved great performance improvements in aerial object detection tasks. However, they still have two significant problems in the detection of tiny objects: i) The preset fixed anchor is not conducive to assigning positive and negative samples in RPN when dealing with tiny objects, resulting in low-quality samples. ii) When the detector encounters tiny objects lacking structural details, it fails to accurately represent features, causing divergence in object features and hindering network learning. In this work, we propose the Anchor Adaptation and Feature Enhancement Strategies (AFS) to alleviate the above two problems. AFS contains two optimized modules: Anchor Adaption RPN Head (A 2 RH) and Feature Enhanced Attention Module (FEAM). Specifically, A 2 RH performs anchor adaptive learning by establishing a new anchor bias learning branch from the feature map, enabling higher-quality positive and negative sample assignments in RPN. FEAM introduces global features and mask attention based on FPN, and presents Gaussian mask supervision for attention to obtain stronger feature representation. Experiments show that our method improves the average precision by 1.8% on the baseline model, and achieves state-of-the-art results on AI-TOD dataset. Moreover, validation on AI-TOD-v2 and VisDrone2019 datasets also confirms the effectiveness of our method.INDEX TERMS deep learning, aerial images, tiny object detection, anchor adaptation, feature enhancement I. INTRODUCTION

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Aerial Object Detection Using Deep Learning: A Review

Cited by 2 publications

References 37 publications

Few-Shot Object Detection in Remote Sensing Imagery via Fuse Context Dependencies and Global Features

Few-Shot Object Detection in Remote Sensing Imagery via Fuse Context Dependencies and Global Features

Effective Anchor Adaptation and Feature Enhancement Strategies for Tiny Object Detection in Aerial Images

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