Consistency- and dependence-guided knowledge distillation for object detection in remote sensing images
Yükleniyor...
Dosyalar
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pergamon-Elsevier Science Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
As one of the challenging tasks in the remote sensing (RS), object detection has been successfully applied in many fields. Convolution neural network (CNN) has recently attracted extensive attention and is widely used in the natural image processing. Nevertheless, RS images have cluttered scenes compared with natural images. As a result, the existing detectors perform poorly in RS images, especially with the complicated backgrounds. Moreover, the detection inference time and model volume of detectors in RS images often go unrecognized. To address the above issues, this study proposes a novel method for object detection in RS images, which is called the consistency- and dependence-guided knowledge distillation (CDKD). To this end, the spatial- and channeloriented structure discriminative modules (SCSDM) are put forward to extract the discriminative spatial locations and channels to which the teacher model pays attention. SCSDM improves the feature representation of the student model by effectively eliminating the influence of noises and the complicated backgrounds. Then, the consistency and dependence of the features between the teacher model and the student model are constructed under the guidance of SCSDM. Experimental results over public datasets for RS images demonstrate that our CDKD method surpasses the state-of-the-art methods effectively. Most of all, on the RSOD dataset, our CDKD method achieves 92% mean average precision with 3.3 M model volume and 588.2 frames per second.
Açıklama
This work was supported by the Natural Science Foundation of Fujian Province of China under Grant No. 2022J06020 and Young Top Talent of Young Eagle Program of Fujian Province, China under Grant No. F21E0011202B01.
Anahtar Kelimeler
Deep Learning, Object Detection, Remote Sensing, Knowledge Distillation, Convolutional Networks, Model
Kaynak
Expert Systems with Applications
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
229
Sayı
Künye
Chen, Y., Lin, M., He, Z., Polat, K., Alhudhaif, A., & Alenezi, F. (2023). Consistency-and dependence-guided knowledge distillation for object detection in remote sensing images. Expert Systems with Applications, 229, 120519.
