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Review of oriented object detection in aerial remote sensing images

  • role： First author第一作者
  • Affiliation：

    School of Computer Science and Technology, Xidian University, Xi'an 710126, China

    Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province, Xi'an 710126, China

  • Email：dangmin@stu.xidian.edu.cn
  • Introduction：党敏，研究方向为动作识别、行为检测、目标检测和多源融合。E-mail：dangmin@stu.xidian.edu.cn
DANG Min13,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Computer Science and Technology, Xidian University, Xi'an 710126, China

    Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China

    Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province, Xi'an 710126, China

  • Email：gliu@xidian.edu.cn
  • Introduction：刘刚，研究方向为嵌入式系统、信息安全、人工智能及应用、图像处理、计算机视觉。E-mail：gliu@xidian.edu.cn
LIU Gang123*,  
  • Affiliation：

    School of Computer Science and Technology, Xidian University, Xi'an 710126, China

    Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province, Xi'an 710126, China

WANG Quan13,  
  • Affiliation：

    School of Computer Science and Technology, Xidian University, Xi'an 710126, China

    Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province, Xi'an 710126, China

ZHANG Yuanze13,  
  • Affiliation：

    School of Computer Science and Technology, Xidian University, Xi'an 710126, China

    Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province, Xi'an 710126, China

WANG Di13,  
  • Affiliation：

    School of Computer Science and Technology, Xidian University, Xi'an 710126, China

    Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province, Xi'an 710126, China

PAN Rong13

Resümee

Die Objekterkennung ist eine zentrale und herausfordernde Aufgabe im Bereich der Computer Vision. In den letzten Jahren hat die Objekterkennung in natürlichen Bildern enorme Erfolge erzielt, und auch die Forschung zur Objekterkennung in Luftbildern hat bedeutende Fortschritte gemacht. Im Gegensatz zu horizontalen Objekten in natürlichen Bildern sind Ziele in Luftbildern oft in beliebigen Richtungen dicht in komplexen und sich ständig verändernden Hintergründen verteilt. Um Ziele präzise und effizient zu lokalisieren und ihre Kategorie zu erkennen, wurde die Aufgabe der Erkennung von rotierenden Objekten auf der Grundlage der horizontalen Erkennung eingeführt. Obwohl die auf tiefem Lernen basierende Erkennung von rotierenden Objekten, insbesondere von Convolutional Neural Networks (CNNs), immer mehr Aufmerksamkeit findet, fehlt es derzeit an systematischer Forschung zu den Herausforderungen. Der Schwerpunkt dieses Artikels liegt auf dem aktuellen Stand der Forschung zur Objekterkennung in Luftbildern und bietet eine systematische Analyse der Herausforderungen bei der Erkennung von rotierenden Objekten mit dem Ziel, die Entwicklung entsprechender Erkennungstechnologien voranzutreiben. Zunächst werden die gängigen Rahmenbedingungen für die horizontale Objekterkennung zusammengefasst, die auch die Grundlage für den Entwurf von Rahmenbedingungen für die Erkennung von rotierenden Objekten bilden. Anschließend wird auf die Hauptprobleme bei der Aufgabe der Erkennung von rotierenden Objekten eingegangen und die wichtigsten Forschungsergebnisse zur Bewältigung jeder Herausforderung sowie deren Vor- und Nachteile zusammengefasst. Des Weiteren werden gängige Datensätze für die Objekterkennung in Luftbildern kurz vorgestellt und die neuesten fortschrittlichen Detektoren für rotierende Objekte auf öffentlich verfügbaren Benchmark-Datensätzen wie DOTA, HRSC2016, DIOR-R und STAR evaluiert und verglichen. Dabei werden nicht nur die signifikanten Erfolge der aktuellen Forschungsergebnisse bestätigt, sondern auch die Einschränkungen bei der Bewältigung extremer geometrischer Formen und komplexer Szenarien aufgezeigt. Schließlich werden die Entwicklungstrends und zukünftigen Forschungsrichtungen bei der Erkennung von rotierenden Objekten in Luftbildern ausführlich diskutiert.

Schlüsselwort

object detection;aerial remote sensing images;Oriented Object Detection;convolutional neural networks;oriented bounding box

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