Infrared and Laser Engineering, Volume. 50, Issue 11, 20210075(2021)
Few-shot prohibited item segmentation algorithm based on graph matching network
Figures & Tables(8)
Fig. 1. Overview of proposed framework in the 1-shot prohibited item segmentation
Fig. 3. Experimental effect results on SIXray dataset. (a) Support image; (b) Support image mask; (c) Query image; (d) Segmentation result, of which red region is the predicted prohibited item region
Fig. 4. Experimental effect results on Xray-PI dataset. (a) Support image; (b) Support image mask; (c) Query image; (d) Segmentation result, of which red region is the predicted prohibited item region
Table 1. Parameter setting of prohibited item segmentation model based on graph matching network
View tableView in ArticleTable 1. Parameter setting of prohibited item segmentation model based on graph matching network
Operational layer Configuration Graph embedding Input image 321×321×3 Convolution layer #maps: 64，k: 7×7，s: 2×2 Maxpool layer w: 3×3，s: 2×2 Convolution layer $\left[ Convolution layer $\left[ Convolution layer $\left[ Convolution layer #maps: 256，k: 1×1，s: 1×1 Graph matching Convolution layer #maps: 256，k: 1×1，s: 1×1 Avgpool layer w: 11×11，s: 1×1 Convolution layer #maps: 256，k: 1×1，s: 1×1 Convolution layer #maps: 256，k: 1×1，s: 1×1 Maxpool layer w: 10×10，s: 1×1 Segmentation Convolution layer #maps: 256，k: 1×1，s: 1×1 Convolution layer $\left[ Convolution layer #maps: 1，k: 1×1，s: 1×1
Table 2. Segmentation performance of model with different filters and length of subgraphs
View tableView in ArticleTable 2. Segmentation performance of model with different filters and length of subgraphs
Filter Size meanIoU Xray-PI SIXray Average filter 5 48.8% 34.0% 10 50.4% 35.8% 15 49.7% 35.5% Maximum filter 5 50.1% 34.8% 10 51.2% 36.4% 15 50.4% 35.3%
Table 3. Segmentation performance of 1-shot task and 5-shot task on SIXray dataset
View tableView in ArticleTable 3. Segmentation performance of 1-shot task and 5-shot task on SIXray dataset
Methods Gun Knife Wrench Pliers Scissors meanIoU 1-shot CANet[ 13 ]40.3 41.6 35.2 33.9 18.3 33.9% PGNet[ 16 ]38.9 41.5 37.4 33.2 18.0 33.8% Ours 41.4 42.1 35.6 34.0 28.5 36.4% 5-shot CANet[ 13 ]43.0 43.2 36.8 35.4 18.9 35.5% PGNet[ 16 ]41.1 42.9 37.0 35.7 19.1 35.2% Ours 43.7 43.4 36.3 35.9 29.3 37.7%
Table 4. Segmentation performance of 1-shot task and 5-shot task on Xray-PI dataset
View tableView in ArticleTable 4. Segmentation performance of 1-shot task and 5-shot task on Xray-PI dataset
Methods Firework Firecracker Bottle Gun Wrench Pliers Blade meanIoU 1-shot CANet[ 13 ]51.4 45.5 42.2 48.1 34.7 53.0 67.9 48.9% PGNet[ 16 ]44.1 41.9 31.8 47.1 36.3 51.1 66.4 45.5% Proposed 52.9 45.7 47.4 51.2 37.5 55.5 68.7 51.2% 5-shot CANet[ 13 ]54.8 47.6 45.2 49.5 35.6 56.1 68.0 51.0% PGNet[ 16 ]46.0 43.0 35.1 48.5 37.1 55.6 68.3 47.7% Proposed 55.4 49.1 48.7 53.6 38.5 56.4 68.9 52.9%
Tools
Get Citation
Copy Citation Text
Zhenyue Zhu, Shujing Lv, Yue Lv. Few-shot prohibited item segmentation algorithm based on graph matching network[J]. Infrared and Laser Engineering, 2021, 50(11): 20210075
Paper Information
Category: Image processing
Received: Jan. 27, 2021
Accepted: --
Published Online: Dec. 7, 2021
The Author Email:
© Copyright 2018-2021 | Chinese Laser Press.
All Rights Reserved 沪ICP备15018463号-20