Jiang, H.; Luo, T.; Peng, H.; Zhang, G. MFCA: Multiscale Feature Context Aggregation Detector for Oriented Object Detection in Remote-Sensing Images. Preprints2023, 2023101631. https://doi.org/10.20944/preprints202310.1631.v1
APA Style
Jiang, H., Luo, T., Peng, H., & Zhang, G. (2023). MFCA: Multiscale Feature Context Aggregation Detector for Oriented Object Detection in Remote-Sensing Images. Preprints. https://doi.org/10.20944/preprints202310.1631.v1
Chicago/Turabian Style
Jiang, H., Hu Peng and Guozheng Zhang. 2023 "MFCA: Multiscale Feature Context Aggregation Detector for Oriented Object Detection in Remote-Sensing Images" Preprints. https://doi.org/10.20944/preprints202310.1631.v1
Abstract
Detecting rotational objects in remote sensing imagery is a significant challenge. These images typically encompass a broad field of view, featuring diverse and intricate backgrounds, with ground objects of various sizes densely scattered. As a result, identifying objects of interest within these images is a daunting task. While the integration of Convolutional Neural Networks (CNN) and Transformer networks leads to some advancements in rotational object detection, there is still room for improvement, particularly in enhancing the extraction and utilization of information related to smaller objects. To address this, our paper presents a multi-scale feature fusion module and a global feature context aggregation module. Initially, we fuse original, shallow, and deep features to reduce the loss of shallow feature information, thereby improving the detection performance of small objects in complex backgrounds. Subsequently, we compute the correlation of contextual information within feature maps to extract valuable insights. We name the newly proposed model the "Multiscale Feature Context Aggregation Module" (MFCA). We evaluate our proposed methodology on three challenging remote sensing datasets: DIOR-R, HRSC, and MAR20. Comprehensive experimental results show that our approach surpasses baseline models by 2.07\% mAP, 1.02\% mAP, and 1.98\% mAP on the DIOR-R, HRSC2016, and MAR20 datasets, respectively.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
