Acta Optica Sinica, Volume. 38, Issue 12, 1215003(2018)
Multi-Objective Detection of Traffic Scenes Based on Improved SSD
Figures & Tables(10)
Fig. 2. Example of CNN model extracting feature convolution kernels at various levels
Fig. 6. Mobile video target detection framework based on time-aware feature mapping
Table 1. Comparison of model identification and detection effects
View tableTable 1. Comparison of model identification and detection effects
Model Dataset AP /% mAP /% P f /%P m /%P d /%P e /%Person Car Cyclist M0 KITTI 73.36 71.53 65.32 70.07 20.21 19.34 41.32 19.13 WD 71.59 69.63 62.75 67.99 19.25 21.38 38.83 20.54 M1 KITTI 87.53 82.16 78.28 82.66 16.48 17.91 57.38 8.23 WD 85.64 80.59 74.34 80.19 18.95 19.28 51.42 10.35 M2 KITTI 77.18 72.35 68.69 72.74 12.31 13.29 57.84 16.56 WD 73.52 70.45 64.83 69.61 15.17 14.49 52.45 17.89 M3 KITTI 88.42 81.73 74.38 81.51 9.53 11.69 64.25 14.53 WD 74.92 72.34 65.63 70.96 16.24 15.19 51.16 17.41 M4 KITTI 92.42 92.23 90.85 91.83 5.19 7.13 81.47 6.21 WD 88.46 87.38 83.24 86.36 8.26 11.27 71.05 9.42
Table 2. Comparison of detection and recognition with different algorithms
View tableTable 2. Comparison of detection and recognition with different algorithms
Method Dataset AP /% mAP /% P d /%FPS /(frame·s-1) Person Car Cyclist Faster R-CNN KITTI 83.26 74.13 75.42 77.61 45.22 13.15 WD 81.49 71.33 68.65 73.82 36.63 11.64 DSOD300 KITTI 77.43 72.26 68.38 72.69 58.68 58.23 WD 70.73 69.39 67.04 69.05 52.32 50.35 DSSD513 KITTI 75.46 69.53 68.34 71.11 59.42 46.34 WD 72.19 68.83 66.45 69.16 49.79 39.38 YOLOv2 544 KITTI 79.43 71.25 67.32 72.66 60.82 56.74 WD 73.29 69.63 68.85 70.59 54.86 49.28 M4 KITTI 92.42 92.23 90.85 91.83 81.47 31.86 WD 88.46 87.38 83.24 86.36 71.05 19.83
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Xia Hua, Xinqing Wang, Dong Wang, Zhaoye Ma, Faming Shao. Multi-Objective Detection of Traffic Scenes Based on Improved SSD[J]. Acta Optica Sinica, 2018, 38(12): 1215003
Paper Information
Category: Machine Vision
Received: May. 22, 2018
Accepted: Jul. 25, 2018
Published Online: May. 10, 2019
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