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Journals >
Laser & Optoelectronics Progress >
Volume 58 >
Issue 2 >
Page 0215003 > Article
Laser & Optoelectronics Progress
Vol. 58, Issue 2, 0215003 (2021)
Multiscale Feature Fusion-Based Object Detection Algorithm
Tao Zhang and Le Zhang
*
Author Affiliations
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
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DOI:
10.3788/LOP202158.0215003
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Tao Zhang, Le Zhang. Multiscale Feature Fusion-Based Object Detection Algorithm[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0215003
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Fig. 1.
Network structure based on multi-scale feature fusion
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Fig. 2.
Network structure of different feature fusion modules. (a) FPN module; (b) LFF module; (c) HFF module
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Fig. 3.
Loss function curves before and after adding feature fusion module. (a) Before adding; (b) after adding
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Fig. 4.
Detection results before and after adding feature fusion module. (a) Before adding; (b) after adding
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Algorithm
Backbone network
mAP /%
SSD300
VGG-16
69.7
RetinaNet
ResNet-50
70.2
Libra RetinaNet
ResNet-50
70.4
Proposed algorithm
ResNet-50
72.6
Table 1.
mAP values of different detection algorithms on PASCAL VOC dataset
Category
SSD300
RetinaNet
Libra
RetinaNet
Proposed
algorithm
Aero
75.7
75.4
75.4
77.4
Bike
78.4
80.2
79.6
80.1
Bird
67.2
72.1
72.1
73.5
Boat
64.4
60.7
64.8
67.3
Bottle
38.9
39.4
37.9
43.1
Bus
79.9
78.9
79.4
81.1
Car
83.3
79.0
79.0
80.3
Cat
84.6
86.4
85.1
87.4
Chair
49.5
52.5
52.6
54.9
Cow
67.1
64.1
68.0
74.1
Category
SSD300
RetinaNet
Libra
RetinaNet
Proposed
algorithm
Table
63.7
67.7
66.5
68.5
Dog
79.2
80.1
80.5
82.2
Horse
80.5
79.4
78.9
81.4
Mbike
79.7
78.2
78.1
79.7
Person
75.2
74.2
74.5
75.6
Plant
37.2
43.0
42.3
45.3
Sheep
69.9
68.4
70.4
70.9
Sofa
68.5
71.3
71.3
72.0
Train
81.6
83.1
83.7
84.1
TV
69.3
70.3
68.8
72.7
Table 2.
AP values of different categories unit: %
HFF module
LFF module
mAP /%
70.4
√
72.0
√
72.2
√
√
72.6
Table 3.
Results of ablation study
Category
LFF module
HFF module
Aero
76.5
77.9
Bike
80.2
80.5
Bird
74.1
72.6
Boat
66.0
66.6
Bottle
40.7
41.9
Bus
78.7
82.4
Car
80.2
79.4
Cat
86.1
86.8
Chair
54.9
53.2
Cow
70.9
69.2
Table
70.2
68.3
Dog
82.2
82.2
Horse
82.5
82.5
Category
LFF module
HFF module
Mbike
79.2
80.5
Person
75.0
75.0
Plant
44.7
45.0
Sheep
69.8
70.7
Sofa
73.5
73.3
Train
83.9
83.4
TV
70.5
71.6
Table 4.
AP values of each category after ablation learning unit: %
Algorithm
Backbone
AP
AP
50
AP
75
AP
S
AP
M
AP
L
SSD512
VGG-16
25.7
44.1
26.6
9.2
29.0
39.0
RetinaNet
ResNet-50
35.6
55.6
38.1
20.8
39.5
46.1
Libra RetinaNet
ResNet-50
37.5
56.9
39.9
22.4
41.4
49.2
Proposed algorithm
ResNet-50
38.8
58.5
41.3
22.9
42.6
50.4
RetinaNet
ResNet-101
37.8
57.5
40.8
20.9
42.1
49.6
Libra RetinaNet
ResNet-101
39.1
58.6
41.7
22.6
43.8
51.4
Proposed algorithm
ResNet-101
40.3
59.9
42.9
23.1
44.8
53.3
Table 5.
AP values of different detection algorithms on MSCOCO dataset unit: %
HFF module
LFF module
AP
AP
50
AP
75
AP
S
AP
M
AP
L
37.5
56.9
39.9
22.4
41.4
49.2
√
38.5
58.3
41.0
22.4
42.6
50.2
√
38.5
58.4
40.9
23.8
42.4
49.7
√
√
38.8
58.5
41.3
22.9
42.6
50.4
Table 6.
Results after ablation learning on COCO val-2017 unit: %
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Tao Zhang, Le Zhang. Multiscale Feature Fusion-Based Object Detection Algorithm[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0215003
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Paper Information
Category: Machine Vision
Received: Jun. 2, 2020
Accepted: Jul. 3, 2020
Published Online: Jan. 8, 2021
The Author Email: Zhang Le (Polaris963@163.com)
DOI:
10.3788/LOP202158.0215003
Recommended Topics
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