Research Article
An Efficient Color Space for Deep-Learning Based Traffic Light Recognition
Table 4
Detection performances (overall mAP and overall AP) of combination methods on test set.
| Combination Method | Overall mAP () | Overall AP () | Ensemble Network Model | Color Space | total | small | non small | green | red | yellow | red left | green left | off |
| Faster R-CNN with Inception-Resnet-v2 | RGB | 20.40 | 15.85 | 36.15 | 33.46 | 23.81 | 4.75 | 34.69 | 17.59 | 8.08 | Normalized RGB | 19.81 | 15.16 | 38.10 | 32.15 | 22.29 | 6.06 | 38.28 | 11.43 | 8.65 | Ruta’s RYG | 18.07 | 13.54 | 33.33 | 28.58 | 20.05 | 2.39 | 35.30 | 17.98 | 4.11 | YCbCr | 16.50 | 12.71 | 31.31 | 29.51 | 15.25 | 4.67 | 31.17 | 14.33 | 4.07 | HSV | 19.70 | 15.41 | 37.06 | 29.23 | 16.91 | 6.74 | 36.00 | 23.54 | 5.77 | CIE Lab | 17.64 | 13.31 | 34.30 | 26.62 | 18.27 | 5.41 | 34.63 | 15.82 | 5.09 |
| Faster R-CNN with Resnet-101 | RGB | 19.24 | 14.67 | 37.91 | 31.21 | 20.73 | 3.79 | 36.92 | 14.34 | 8.44 | Normalized RGB | 17.57 | 13.54 | 32.86 | 29.70 | 18.20 | 4.87 | 33.67 | 11.99 | 6.98 | Ruta’s RYG | 14.72 | 11.21 | 28.42 | 26.55 | 16.62 | 4.71 | 26.27 | 10.14 | 4.05 | YCbCr | 12.36 | 9.49 | 25.02 | 24.03 | 10.02 | 2.83 | 26.36 | 8.84 | 2.05 | HSV | 15.76 | 11.11 | 32.24 | 25.07 | 14.77 | 5.64 | 23.06 | 17.99 | 8.01 | CIE Lab | 10.90 | 7.63 | 23.73 | 19.98 | 13.79 | 3.67 | 20.43 | 5.28 | 2.28 |
| R-FCN with Resnet-101 | RGB | 16.63 | 11.85 | 37.27 | 28.47 | 13.00 | 4.92 | 30.19 | 18.32 | 4.85 | Normalized RGB | 14.50 | 10.95 | 29.97 | 23.57 | 14.41 | 2.50 | 27.87 | 14.59 | 4.08 | Ruta’s RYG | 14.21 | 10.33 | 26.66 | 20.89 | 9.08 | 3.01 | 32.75 | 13.77 | 5.72 | YCbCr | 13.06 | 9.44 | 25.05 | 21.43 | 10.01 | 2.50 | 24.49 | 14.63 | 5.28 | HSV | 14.66 | 10.59 | 29.52 | 25.40 | 9.99 | 3.17 | 28.39 | 15.23 | 5.78 | CIE Lab | 12.24 | 9.06 | 23.51 | 14.58 | 12.43 | 1.93 | 27.87 | 11.80 | 4.85 |
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