Oriented object detection in satellite images using convolutional neural network based on ResNeXt

Asep Haryono, Grafika Jati, Wisnu Jatmiko

Research output: Contribution to journalArticlepeer-review

Abstract

Most object detection methods use a horizontal bounding box that causes problems between adjacent objects with arbitrary directions, resulting in misaligned detection. Hence, the horizontal anchor should be replaced by a rotating anchor to determine oriented bounding boxes. A two-stage process of delineating a horizontal bounding box and then converting it into an oriented bounding box is inefficient. To improve detection, a box-boundary-aware vector can be estimated based on a convolutional neural network. Specifically, we propose a ResNeXt101 encoder to overcome the weaknesses of the conventional ResNet, which is less effective as the network depth and complexity increase. Owing to the cardinality of using a homogeneous design and multibranch architecture with few hyperparameters, ResNeXt captures better information than ResNet. Experimental results demonstrate more accurate and faster oriented object detection of our proposal compared with a baseline, achieving a mean average precision of 89.41% and inference rate of 23.67 fps.

Original languageEnglish
JournalETRI Journal
DOIs
Publication statusAccepted/In press - 2023

Keywords

  • box-boundary-aware vector
  • convolutional neural network
  • oriented object detection
  • ResNeXt101
  • satellite imagery

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