.png){kind=icon}
.png){kind=icon}
Return
FAR-Net: Fast Anchor Refining for Arbitrary-Oriented Object Detection
.png){kind=icon}
PRE
AI
DOI：10.1109/LGRS.2022.3144513.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Compared with natural images, targets in remote-sensing images are often distributed with more flexible orientation, aspect ratio, and scale. Thus, anchor-based algorithms often employ plenty of preset anchors to encode the above-mentioned attributes in object detection tasks. However, they often suffer from the following issues: 1) significant computational burden caused by dense-sampling anchors; 2) serious background interference since many anchors only cover small parts of the actual target; and 3) feature misalignment between the targets with the preset anchors due to the absence of the most discriminant features for target extraction. Therefore, in this letter, a fast anchor refining network (FAR-Net) is advocated to address the remaining issues for arbitrary-oriented object detection in the remote-sensing field. To be specific, a rotation alignment module (RAM) and balanced regression loss function (BR-loss) are carefully designed in the FAR-Net. The RAM is capable of generating high-quality anchors based on a refinement convolution and adaptively aligning the convolutional features by complying with the anchor boxes to reduce redundant calculation. The BR-loss is designed by employing a balanced loss function to prevent misaligned anchors from causing major gradient descents, thereby achieving a more stable network training procedure. Extensive experiments on public remote-sensing datasets (HRSC2016 and UCAS-AOD) demonstrate the excellent detection performance of our algorithm in comparison with numerous existing detectors.
Keywords:
Remote sensing
Random access memory
Training
Object detection
Feature extraction
Task analysis
Sensors
Convolutional neural network (CNN)
oriented object detection
remote-sensing images
rotation alignment
Journal
IF:
16.4
Papers:
1.0W
Citations:
5.1K