Deep Binary Classification via Multi-Resolution Network and Stochastic Orthogonality for Subcompact Vehicle Recognition
Abstract
:1. Introduction
2. Related Works
2.1. Support Vector Machine
2.2. Neural Network
2.3. Convolutional Neural Network (CNN)
3. Proposed Method
3.1. Subcompact Vehicle Dataset
3.2. Pre-Convolution Layer
3.3. Multi-Resolution Network
3.4. Orthogonal Learning
4. Experimental Results
4.1. Quantitative Evaluation
4.2. Baseline Comparison
4.3. Visualization
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Method | TP | FN | TN | FP | Precision | Recall | FPR | Acc. |
---|---|---|---|---|---|---|---|---|
165 | 335 | 912 | 588 | 0.2191 | 0.3300 | 0.7809 | 53.85% | |
43 | 457 | 1477 | 23 | 0.6515 | 0.0860 | 0.3485 | 76.00% | |
37 | 463 | 1455 | 45 | 0.4512 | 0.0740 | 0.5488 | 74.60% | |
182 | 318 | 1377 | 123 | 0.5967 | 0.3650 | 0.4033 | 77.95% | |
198 | 302 | 1457 | 43 | 0.8216 | 0.3960 | 0.1784 | 82.75% | |
410 | 90 | 1474 | 26 | 0.9404 | 0.8200 | 0.0596 | 94.20% | |
373 | 127 | 1444 | 56 | 0.8695 | 0.7460 | 0.1305 | 90.85% | |
417 | 83 | 1478 | 22 | 0.9499 | 0.8340 | 0.0501 | 94.75% | |
423 | 77 | 1477 | 23 | 0.9484 | 0.8460 | 0.0516 | 95.00% |
Method | Baseline | Tool | Accuracy (Binary) | Accuracy (Multi) | Proc. Time (ms) | GPU-Memory (GB) |
---|---|---|---|---|---|---|
CNN | VGG16 | Tensorflow | 0.8275 | 0.8010 | 65 ms | 1.3 GB |
CNN | Resnet50 | Pytorch | 0.8740 | 0.8435 | 80 ms | 1.0 GB |
CNN | Resnext50 | Pytorch | 0.8890 | 0.8575 | 86 ms | 1.0 GB |
MRN | VGG16 | Tensorflow | 0.9500 | 0.9095 | 70 ms | 1.6 GB |
MRN | Resnet50 | Pytorch | 0.9290 | 0.8810 | 100 ms | 1.2 GB |
MRN | Resnext50 | Pytorch | 0.9530 | 0.8955 | 106 ms | 1.3 GB |
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Shin, J.; Koo, B.; Kim, Y.; Paik, J. Deep Binary Classification via Multi-Resolution Network and Stochastic Orthogonality for Subcompact Vehicle Recognition. Sensors 2020, 20, 2715. https://doi.org/10.3390/s20092715
Shin J, Koo B, Kim Y, Paik J. Deep Binary Classification via Multi-Resolution Network and Stochastic Orthogonality for Subcompact Vehicle Recognition. Sensors. 2020; 20(9):2715. https://doi.org/10.3390/s20092715
Chicago/Turabian StyleShin, Joongchol, Bonseok Koo, Yeongbin Kim, and Joonki Paik. 2020. "Deep Binary Classification via Multi-Resolution Network and Stochastic Orthogonality for Subcompact Vehicle Recognition" Sensors 20, no. 9: 2715. https://doi.org/10.3390/s20092715
APA StyleShin, J., Koo, B., Kim, Y., & Paik, J. (2020). Deep Binary Classification via Multi-Resolution Network and Stochastic Orthogonality for Subcompact Vehicle Recognition. Sensors, 20(9), 2715. https://doi.org/10.3390/s20092715