Laser & Optoelectronics Progress, Volume. 58, Issue 2, 0210013(2021)
Vehicle Appearance Recognition Using Shared Lightweight Convolutional Neural Networks
Figures & Tables(11)
Fig. 3. Structure of networks. (a) No-shared; (b) partly-shared; (c) fully-shared
Fig. 6. Result comparison between different “slimming” SqueezeNet. (a) Training loss of color recognition; (b) validation accuracy of color recognition; (c) training loss of vehicle type recognition; (d) validation accuracy of vehicle type recognition
Fig. 7. Result comparison for vehicle type recognition. (a) Training loss; (b) validation accuracy
Fig. 8. Result comparison for vehicle color recognition. (a) Training loss; (b) validation accuracy
Table 1. Three “slimming” SqueezeNet structures
View tableTable 1. Three “slimming” SqueezeNet structures
Number Layer Filter shape (N: number of categories) SqueezeNet Squeeze1 Squeeze2 Squeeze3 0 Conv1 96 × 3 × 7 ×7 48 × 3 × 7 ×7 24 × 3 × 7 ×7 12 × 3 × 7 ×7 1 Fire2/Squeeze1×1 16 × 96 × 1 × 1 8 × 48 × 1 × 1 4 × 24 × 1 × 1 2 × 12 × 1 × 1 2 Fire2/Expand3×3 64 × 16 × 3 × 3 32 × 8 × 3 × 3 16 × 4 × 3 × 3 8 × 2 × 3 × 3 3 Fire3/Squeeze1×1 16 × 128 × 1 × 1 8 × 64 × 1 × 1 4 × 32 × 1 × 1 2 × 16 × 1 × 1 4 Fire3/Expand3×3 64 × 16 × 3 × 3 32 × 8 × 3 × 3 16 × 4 × 3 × 3 8 × 2 × 3 × 3 5 Fire4/Squeeze1×1 32 × 128 × 1 × 1 16 × 64 × 1 × 1 8 × 32 × 1 × 1 4 × 16 × 1 × 1 6 Fire4/Expand3×3 128 × 32 × 3 × 3 64 × 16 × 3 × 3 32 × 8 × 3 × 3 16 × 4 × 3 × 3 7 Fire5/Squeeze1×1 32 × 256 × 1 × 1 16 × 128 × 1 × 1 8 × 64 × 1 × 1 4 × 32 × 1 × 1 8 Fire5/Expand3×3 128 × 32 × 3 × 3 64 × 16 × 3 × 3 32 × 8 × 3 × 3 16 × 4 × 3 × 3 9 Fire6/Squeeze1×1 48 × 256 × 1 × 1 24 × 128 × 1 × 1 12 × 64 × 1 × 1 6 × 32 × 1 × 1 10 Fire6/Expand3×3 192 × 48 × 3 × 3 96 × 24 × 3 × 3 48 × 12 × 3 × 3 24 × 6 × 3 × 3 11 Fire7/Squeeze1×1 48 × 384 × 1 × 1 24 × 192 × 1 × 1 12 × 96 × 1 × 1 6 × 48 × 1 × 1 Number Layer Filter shape (N: number of categories) SqueezeNet Squeeze1 Squeeze2 Squeeze3 12 Fire7/Expand3×3 192 × 48 × 3 × 3 96 × 24 × 3 × 3 48 × 12 × 3 × 3 24 × 6 × 3 × 3 13 Fire8/Squeeze1×1 64 × 384 × 1 × 1 32 × 192 × 1 × 1 16 × 96 × 1 × 1 8 × 48 × 1 × 1 14 Fire8/Expand3×3 256 × 64 × 3 × 3 128 × 32 × 3 × 3 64 × 16 × 3 × 3 32 × 8 × 3 × 3 15 Fire9/Squeeze1×1 64 × 512 × 1 × 1 32 × 256 × 1 × 1 16 × 128 × 1 × 1 8 × 64 × 1 × 1 16 Fire9/Expand3×3 256 × 64 × 3 × 3 128 × 32 × 3 × 3 64 × 16 × 3 × 3 32 × 8 × 3 × 3 17 Conv10 N × 512 × 1 × 1 N × 256 × 1 × 1 N × 128 × 1 × 1 N × 64 × 1 × 1 18 Global average pooling+SoftMax
Table 2. Comparison of number of parameters in nine network structures
View tableTable 2. Comparison of number of parameters in nine network structures
Network(basic network+sharing method) Number of trainable parameters Number of non-trainable parameters Number of total parameters Squeeze1+no-shared 338825 838 339663 Squeeze1+partly-shared 313993 678 314671 Squeeze1+fully-shared 171873 438 172311 Squeeze2+no-shared 90401 438 90839 Squeeze2+partly-shared 82305 358 82663 Squeeze2+fully-shared 46445 238 46683 Squeeze3+no-shared 25469 238 25707 Squeeze3+partly-shared 22501 198 22699 Squeeze3+fully-shared 13371 138 13509
Table 3. Result comparison between different networks
View tableTable 3. Result comparison between different networks
Network(basic network+sharing method) Testing accuracy/% Time cost/ms Color Type Squeeze1+no-shared 98.6 99.2 6.89 Squeeze1+partly-shared 98.5 99.2 4.99 Squeeze1+fully-shared 98.5 99.1 4.42 Squeeze2+no-shared 98.6 99.2 4.11 Squeeze2+partly-shared 98.5 99.0 2.79 Squeeze2+fully-shared 98.4 98.8 2.48 Squeeze3+no-shared 98.3 99.0 3.06 Squeeze3+partly-shared 98.0 98.3 2.01 Squeeze3+fully-shared 97.5 97.0 1.68
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Qing Kang, Hongdong Zhao, Dongxu Yang. Vehicle Appearance Recognition Using Shared Lightweight Convolutional Neural Networks[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0210013
Paper Information
Category: Image Processing
Received: Jun. 30, 2020
Accepted: Jul. 10, 2020
Published Online: Jan. 8, 2021
The Author Email: Zhao Hongdong (zhaohd@hebut.edu.cn)
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