Oriented Object Detection in Remote Sensing Using an Enhanced Feature Pyramid Network
-
Published:2023-08-23
Issue:17
Volume:12
Page:3559
-
ISSN:2079-9292
-
Container-title:Electronics
-
language:en
-
Short-container-title:Electronics
Author:
Zhu Xinyu1, Zhou Wei1, Wang Kun1, He Bing1, Fu Ying1, Wu Xi1, Zhou Jiliu2
Affiliation:
1. College of Computer Science&Technology, Chengdu University of Information Technology, Chengdu 610225, China 2. Images and Spatial Information 2011 Collaborative Innovation Center of Sichuan Province, Chengdu 610225, China
Abstract
Object detection in remote sensing images is a critical task within the field of remote sensing image interpretation and analysis, serving as a fundamental foundation for military surveillance and traffic guidance. Recently, although many object detection algorithms have been improved to adapt to the characteristics of remote sensing images and have achieved good performance, most of them still use horizontal bounding boxes, which struggle to accurately mark targets with multiple angles and dense arrangements in remote sensing images. We propose an oriented bounding box optical remote sensing image object detection method based on an enhanced feature pyramid, and add an attention module to suppress background noise. To begin with, we incorporate an angle prediction module that accurately locates the detection target. Subsequently, we design an enhanced feature pyramid network, utilizing deformable convolutions and feature fusion modules to enhance the feature information of rotating targets and improve the expressive capacity of features at all levels. The proposed algorithm in this paper performs well on the public DOTA dataset and HRSC2016 dataset, compared with other object detection methods, and the detection accuracy AP values of most object categories are improved by at least three percentage points. The results show that our method can accurately locate densely arranged and dynamically oriented targets, significantly reducing the risk of missing detections, and achieving higher levels of target detection accuracy.
Funder
Sichuan Natural Science Foundation Sichuanl Key Research and Development Program Project of Innovation Ability Enhancement of Chengdu University of Information Technology Sichuan Science and Technology Program
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference49 articles.
1. Qiu, H., Ma, Y., Li, Z., Liu, S., and Sun, J. (2020, January 23–28). Borderdet: Border feature for dense object detection. Proceedings of the Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK. 2. Shen, Z., Liu, Z., Li, J., Jiang, Y.G., Chen, Y., and Xue, X. (2017, January 22–29). Dsod: Learning deeply supervised object detectors from scratch. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy. 3. Arbitrary-oriented scene text detection via rotation proposals;Ma;IEEE Trans. Multimed.,2018 4. Kim, K., and Lee, H.S. (2020, January 23–28). Probabilistic anchor assignment with iou prediction for object detection. Proceedings of the Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK. 5. Fast and accurate multi-class geospatial object detection with large-size remote sensing imagery using CNN and Truncated NMS;Shen;ISPRS J. Photogramm. Remote Sens.,2022
|
|