Acta Optica Sinica, Volume. 39, Issue 8, 0815005(2019)
An Improved Multi-Gate Feature Pyramid Network
Figures & Tables(12)
Fig. 5. Visual comparison of SSD algorithm and MSSD algorithm. (a)(c)(e)(g) SSD algorithm; (b)(d)(f)(h) MSSD algorithm
Table 1. Comparison between SSD network with FPN structure and MSSD network
View tableTable 1. Comparison between SSD network with FPN structure and MSSD network
Method Feature layers mAP /% FPS SSD[ 6 ]Conv4-Pool11 77.5 46(Titan X) SSD+FPN Conv4-Conv9 77.7 53.9 MSSD Conv4-Pool11 78.8 27.1 MSSD Conv4-Conv9 79.0 31.7
Table 2. Comparison of improved effects of fusion structure
View tableTable 2. Comparison of improved effects of fusion structure
Conv 1×1 Feature fusion mode Deconv or bilinear interpolation mAP /% FPS √ Sum Deconv 78.9 31.2 Sum Deconv 79.0 31.7 √ Sum Bilinear interpolation 78.5 40.1 Sum Bilinear interpolation 78.5 40.2 Max Deconv 78.2 36.4 Max Bilinear interpolation 76.6 42.2
Table 3. Analysis of effectiveness of memory and filter channels
View tableTable 3. Analysis of effectiveness of memory and filter channels
Number Forget gate Input gate Output gate mAP /% 1 √ / 2 √ 75.2 3 √ / 4 √ √ 78.6 5 √ √ / 6 √ √ 77.9 7 √ √ √ 79
Table 4. Performance comparison of different basic networks
View tableTable 4. Performance comparison of different basic networks
Method Network mAP /% FPS SSD[ 11 ]ResNet101 77.1 18.9(Titan X) SSD+FPN ResNet101 78.7 39.1 MSSD ResNet101 79.3 23.7 MSSD VGG16 79.0 31.7
Table 5. Accuracy comparison of various advanced deep learning algorithms on VOC2007 dataset
View tableTable 5. Accuracy comparison of various advanced deep learning algorithms on VOC2007 dataset
Method Network mAP /% Aero Bike Bird Boat Bottle Bus Car Cat Chair Cow Table Dog Horse Mbike Person Plant Sheep Sofa Train Tv Average Faster[ 5 ]VGG16 76.5 79.0 70.9 65.5 52.1 83.1 84.7 86.4 52.0 81.9 65.7 84.8 84.6 77.5 76.7 38.8 73.6 73.9 83.0 72.6 73.2 ION[ 23 ]VGG16 79.2 83.1 77.6 65.6 54.9 85.4 85.1 87.0 54.4 80.6 73.8 85.3 82.2 82.2 74.4 47.1 75.8 72.7 84.2 80.4 75.6 Faster[ 18 ]ResNet101 79.8 80.7 76.2 68.3 55.9 85.1 85.3 89.8 56.7 87.8 69.4 88.3 88.9 80.9 78.4 41.7 78.6 79.8 85.3 72.0 76.4 MR-CNN[ 24 ]VGG16 80.3 84.1 78.5 70.8 68.5 88.0 85.9 87.8 60.3 85.2 73.7 87.2 86.5 85.0 76.4 48.5 76.3 75.5 85.0 81.0 78.2 R-FCN[ 22 ]ResNet101 79.9 87.2 81.5 72.0 69.8 86.8 88.5 89.8 67.0 88.1 74.5 89.8 90.6 79.9 81.2 53.7 81.8 81.5 85.9 79.9 80.5 SSD300[ 6 ]VGG16 79.5 83.9 76.0 69.6 50.5 87.0 85.7 88.1 60.3 81.5 77.0 86.1 87.5 83.97 79.4 52.3 77.9 79.5 87.6 76.8 77.5 SSD512[ 6 ]VGG16 84.8 85.1 81.5 73.0 57.8 87.8 88.3 87.4 63.5 85.4 73.2 86.2 86.7 83.9 82.5 55.6 81.7 79.0 86.6 80.0 79.5 DSSD321[ 11 ]ResNet101 81.9 84.9 80.5 68.4 53.9 85.6 86.2 88.9 61.1 83.5 78.7 86.7 88.7 86.7 79.7 51.7 78.0 80.9 87.2 79.4 78.6 DSSD513[ 11 ]ResNet101 86.6 86.2 82.6 74.9 62.5 89.0 88.7 88.8 65.2 87.0 78.7 88.2 89.0 87.5 83.7 51.1 86.3 81.6 85.7 83.7 81.5 MDSSD300[ 25 ]VGG16 86.5 87.6 78.9 70.6 55.0 86.9 87.0 88.1 58.5 84.8 73.4 84.8 89.2 88.1 78.0 52.3 78.6 74.5 86.8 80.7 78.6 MDSSD512[ 25 ]VGG16 88.8 88.7 83.2 73.7 58.3 88.2 89.3 87.4 62.4 85.1 75.1 84.7 89.7 88.3 83.2 56.7 84.0 77.4 83.9 77.6 80.3 YOLOV3[ 8 ]Darknet53 85.5 85.5 75.6 70.0 66.5 87.6 87.7 89.4 64.3 83.5 73.6 85.9 86.9 86.2 83.3 56.2 75.3 78.0 86.4 77.8 79.2 MSSD300 ResNet101 81.2 87.2 78.7 72.7 53.4 86.4 85.6 89.1 63.1 84.5 80.0 87.5 88.9 84.8 78.8 54.5 80.9 83.2 87.1 77.4 79.3 MSSD300 VGG16 81.6 85.8 78.0 74.0 55.3 86.2 86.5 88.2 64.6 85.9 76.9 85.4 87.7 85.2 79.5 51.1 78.8 80.8 87.9 78.6 79.0 MSSD500 VGG16 87.6 87.2 83.7 75.5 57.8 86.7 88.4 89.5 66.3 84.6 78.9 86.8 88.1 86.4 83.3 58.0 81.4 81.0 88.4 78.1 81.0
Table 6. Comparison of test results of various improved algorithms based on SSD
View tableTable 6. Comparison of test results of various improved algorithms based on SSD
Method Network FPS GPU #proposals Input size /(pixel×pixel) mAP /% SSD300[ 6 ]VGG16 46 Titan X 8732 300×300 77.2 SSD512[ 6 ]VGG16 19 Titan X 24564 512×512 78.5 MDSSD300[ 25 ]VGG16 38.5 1080Ti 44530 300×300 78.6 MDSSD512[ 25 ]VGG16 17.3 1080Ti - 512×512 80.3 DSSD321[ 11 ]ResNet101 9.5 Titan X 17088 321×321 78.6 DSSD513[ 11 ]ResNet101 5.5 Titan X 43688 513×513 81.5 RSSD300[ 14 ]VGG16 35 Titan X 8732 300×300 78.5 RSSD512[ 14 ]VGG16 16.6 Titan X 24564 512×512 80.8 MSSD300 ResNet101 23.7 1080Ti 8728 300×300 79.3 MSSD300 VGG16 31.7 1080Ti 8732 300×300 79.0 MSSD512 VGG16 17.3 1080Ti 24564 512×512 81.0
Table 7. Detection accuracy of small targets by SSD algorithm and MSSD algorithm
View tableTable 7. Detection accuracy of small targets by SSD algorithm and MSSD algorithm
Method mAP /% SSD[ 6 ]55.4 MSSD 56.3
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Tong Zhao, Jieyu Liu, Qiang Shen. An Improved Multi-Gate Feature Pyramid Network[J]. Acta Optica Sinica, 2019, 39(8): 0815005
Paper Information
Category: Machine Vision
Received: Mar. 13, 2019
Accepted: Apr. 22, 2019
Published Online: Aug. 7, 2019
The Author Email: Jieyu Liu (601080018@qq.com)
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