X-Ray Image Controlled Knife Detection and Recognition Based on Improved SSD

Table 1. Results of each network performance test
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Table 1. Results of each network performance test
Network Speed /(frame·s^-1) Accuracy Mode size /M FLOPS /10⁹
DenseNet121 114 74.3 32 1.9
VGG16 183 71.7 537 15.3
MobileNetv2 231 72.0 14 0.52
ShuffleNet 303 70.8 21 0.524
ResNet34 402 73.1 87 3.6

Table 2. Experimental environment configuration
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Table 2. Experimental environment configuration
Hardware Software
CPU: Intel i7-9750H CPU Operating system
GTX 1660Ti-8G Ubuntu16.04
GPU: NVIDIA RTX2080ti Frame: Tensorflow
RAM: 11G×4 Language: Python

Table 3. mAP of each algorithm on SDCK dataset unit: %
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Table 3. mAP of each algorithm on SDCK dataset unit: %
Method SSD DSSD MFDSSD Our method
mAP 89.8 91.3 90.5 92.6

Table 4. mAP of SSD algorithm on SDCK controlled tool dataset unit: %
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Table 4. mAP of SSD algorithm on SDCK controlled tool dataset unit: %
Method SSD Kitchen knife Fruit knife Hacking knife Dagger Scissor Spanner Little knife
mAP 89.8 91.0 88.9 93.0 89.2 87.8 91.3 87.5

Table 5. mAP of each algorithm on VOC2007+2012 dataset
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Table 5. mAP of each algorithm on VOC2007+2012 dataset
Method Train Test Input size mAP /%
SSD 2007+2012 2007 300×300 78.8
DSSD 2007+2012 2007 321×321 80.3
MFDSSD 2007+2012 2007 300×300 80.0
Our method 2007+2012 2007 512×512 80.5

Table 6. Detection speed of each algorithm on SDCK datasetunit: frame·s^-1
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Table 6. Detection speed of each algorithm on SDCK datasetunit: frame·s^-1
Method SSD DSSD MFDSSD Our method
Speed 18.3 9.6 13.5 16.7

Table 7. Improved algorithm testmAP step by step
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Table 7. Improved algorithm testmAP step by step
Base net Function module mAP /%
VGG16 — 89.8
ResNet34 — 90.8
VGG16 √ 90.5
ResNet34 √ 92.6

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Ruihong Guo, Li Zhang, Ying Yang, Yang Cao, Junxi Meng. X-Ray Image Controlled Knife Detection and Recognition Based on Improved SSD[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0404001

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