• Acta Optica Sinica
  • Vol. 38, Issue 1, 0111005 (2018)
Yuqingyang Hou*, Jicheng Quan, and Yongming Wei
Author Affiliations
  • Laboratory of Digital Earth Science, Aviation University of Air Force, Changchun, Jilin 130000, China
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    DOI: 10.3788/AOS201838.0111005 Cite this Article Set citation alerts
    Yuqingyang Hou, Jicheng Quan, Yongming Wei. Valid Aircraft Detection System for Remote Sensing Images Based on Cognitive Models[J]. Acta Optica Sinica, 2018, 38(1): 0111005 Copy Citation Text show less
    Structural diagram of detection system
    Fig. 1. Structural diagram of detection system
    Workflow chart of system
    Fig. 2. Workflow chart of system
    Structural diagram of SSD network
    Fig. 3. Structural diagram of SSD network
    Structure of full convolution segmentation network
    Fig. 4. Structure of full convolution segmentation network
    Process diagram of filtering out invalid targets
    Fig. 5. Process diagram of filtering out invalid targets
    Training process of system
    Fig. 6. Training process of system
    Airport detection samples
    Fig. 7. Airport detection samples
    Detection samples for unshaded aircrafts
    Fig. 8. Detection samples for unshaded aircrafts
    Detection samples for shaded aircrafts
    Fig. 9. Detection samples for shaded aircrafts
    Contrast between (a) artificial segmentation image and (b) original image
    Fig. 10. Contrast between (a) artificial segmentation image and (b) original image
    Sketch of fIoU definition
    Fig. 11. Sketch of fIoU definition
    (a) Training accuracy and (b) loss parameter versus iteration number in classification training process on VGGNet
    Fig. 12. (a) Training accuracy and (b) loss parameter versus iteration number in classification training process on VGGNet
    Aircraft detection results
    Fig. 13. Aircraft detection results
    Airport segmentation results
    Fig. 14. Airport segmentation results
    Screening results from aircraft detection
    Fig. 15. Screening results from aircraft detection
    TargetfAPAverage fIoUTime /(ms·frame-1)
    Airport76.610.64500
    Aircraft85.460.9510
    Shaded aricraft80.230.8913
    Table 1. Detection results
    AlgorithmfAPEfficient aircraft ratioTime /(ms·frame-1)
    DPM80.6154.2%670
    SSD40.1263.1%120
    Recognitive model75.2394.5%528
    Table 2. Performance contrast among different detection algorithms