A New Method of Different Neural Network Depth and Feature Map Size on Remote Sensing Small Target Detection
Objetos digitales
| Tag | 1 | 2 | Value |
|---|---|---|---|
| LDR | 00000cab a2200000 4500 | ||
| 001 | MAP20210031236 | ||
| 003 | MAP | ||
| 005 | 20220911185812.0 | ||
| 008 | 211027e20211004esp|||p |0|||b|spa d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a922.134 | ||
| 100 | 1 | $0MAPA20210034671$aCao, Yaming | |
| 245 | 1 | 0 | $aA New Method of Different Neural Network Depth and Feature Map Size on Remote Sensing Small Target Detection$cYaming Cao, Zhen Yang, Chen Gao |
| 520 | $aConvolutional neural networks (CNNs) have shown strong learning capabilities in computer vision tasks such as classification and detection. Especially with the introduction of excellent detection models such as YOLO (V1, V2 and V3) and Faster R-CNN, CNNs have greatly improved detection efficiency and accuracy. However, due to the special angle of view, small size, few features, and complicated background, CNNs that performs well in the ground perspective dataset, fails to reach a good detection accuracy in the remote sensing image dataset. To this end, based on the YOLO V3 model, we used feature maps of different depths as detection outputs to explore the reasons for the poor detection rate of small targets in remote sensing images by deep neural networks. We also analyzed the effect of neural network depth on small target detection, and found that the excessive deep semantic information of neural network has little effect on small target detection. Finally, the verification on the VEDAI dataset shows, that the fusion of shallow feature maps with precise location information and deep feature maps with rich semantics in the CNNs can effectively improve the accuracy of small target detection in remote sensing images. | ||
| 540 | $aLa copia digital se distribuye bajo licencia "Attribution 4.0 International (CC BY NC 4.0)"$f$uhttps://creativecommons.org/licenses/by-nc/4.0$964 | ||
| 650 | 4 | $0MAPA20080611200$aInteligencia artificial | |
| 700 | 1 | $0MAPA20210034688$aYang, Zhen | |
| 700 | 1 | $0MAPA20210034695$aGao, Chen | |
| 773 | 0 | $wMAP20200034445$g04/10/2021 Volumen 24 Número 68 - octubre 2021 , p. 21-32$x1988-3064$tRevista Iberoamericana de Inteligencia Artificial$d : IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018- | |
| 856 | $qapplication/pdf$w888$yRecurso electrónico / Electronic resource |