• Laser & Optoelectronics Progress
  • Vol. 59, Issue 18, 1815016 (2022)
Guangda Xie1、2, Yang Li2、*, Hongquan Qu2, and Zaiming Sun3
Author Affiliations
  • 1School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
  • 2Information College, North China University of Technology, Beijing 100144, China
  • 3School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
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    DOI: 10.3788/LOP202259.1815016 Cite this Article Set citation alerts
    Guangda Xie, Yang Li, Hongquan Qu, Zaiming Sun. Small Target Accurate Vehicle Detection Algorithm Based on Improved Transformer[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1815016 Copy Citation Text show less
    Overall architecture of image enhancement model
    Fig. 1. Overall architecture of image enhancement model
    Generator (G) (up) and Transformer block (down)
    Fig. 2. Generator (G) (up) and Transformer block (down)
    Edge-enhancement network
    Fig. 3. Edge-enhancement network
    Swin Transformer detection process
    Fig. 4. Swin Transformer detection process
    Swin Transformer block and SW-MSA. (a) Swin Transformer block; (b) W-MSA; (c) SW-MSA
    Fig. 5. Swin Transformer block and SW-MSA. (a) Swin Transformer block; (b) W-MSA; (c) SW-MSA
    Vehicle detection process
    Fig. 6. Vehicle detection process
    Examples of UA-DETRAC dataset
    Fig. 7. Examples of UA-DETRAC dataset
    Training process of Swin Transformer and proposed model. (a) Swin Transformer; (b) proposed model
    Fig. 8. Training process of Swin Transformer and proposed model. (a) Swin Transformer; (b) proposed model
    Contrast of sharpness and detection effect between original image and image enhancement. (a) (b) (c) (d) Illumination change; (e) (f) (g) (h) blurred, dense small targets; (i) (j) (k) (l) long range small targets
    Fig. 9. Contrast of sharpness and detection effect between original image and image enhancement. (a) (b) (c) (d) Illumination change; (e) (f) (g) (h) blurred, dense small targets; (i) (j) (k) (l) long range small targets
    TestsetModelBackboneAP /%Time /ms
    UA-DETRACCascade R-CNNResNet-10179.848.8
    UA-DETRACYOLOV3Darknet-5387.642.3
    UA-DETRACSwin-TransformerSwin-T96.465.3
    UA-DETRACProposed modelEnhance-Swin-T99.068.4
    VOC-vehicleCascade R-CNNResNet-10186.748.8
    VOC-vehicleYOLOV3Darknet-5390.942.3
    VOC-vehicleSwin-TransformerSwin-T95.265.3
    VOC-vehicleProposed modelEnhance-Swin-T98.168.4
    Small-100Cascade R-CNNResNet-10149.348.8
    Small-100YOLOV3Darknet-5357.942.3
    Small-100Swin-TransformerSwin-T80.465.3
    Small-100Proposed modelEnhance-Swin-T88.368.4
    Table 1. Comparison results of different detection algorithm performance
    Guangda Xie, Yang Li, Hongquan Qu, Zaiming Sun. Small Target Accurate Vehicle Detection Algorithm Based on Improved Transformer[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1815016
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