An improved semi-supervised transfer learning method for infrared object detection neural network

Algorithm 1: SSTL
Input: Detection neural network and the parameters , RGB dataset , labeled IR dataset , unlabeled IR dataset , weight of unsupervised loss , .
Output: Trained neural network
1. Initialize parameters of neural network , weight of unsupervised loss ;
2. Pre-train on RGB dataset ;
3. Adjust the number of output channels of classifier according to the number of categories of IR labels;
4. FOR Epoch t
5. 　 FOR Each Batch
6. 　　Sampling BatchSize training data form and ;
7. 　　Getting the predictions , of batch images through forward propagation;
8. 　　Calculate the loss of predictions for supervised samples with Eq.(1);
9. 　　For each , find the neighbourhood ;
10. 　　For each , find the neighbourhood ;
11. 　　Calculate the unsupervised loss of predictions through Eq.(7);
12. 　　Calculate the gradient for semi-supervised loss Eq.(2);
13. 　　Update ;
14. 　 END FOR
15. 　Update the weight of unsupervised loss α with Eq.(9);
16. END FOR

Table 2. [in Chinese]
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Table 2. [in Chinese]
Classification Training Test Total
Labeled Unlabeled
Launcher 36 33 38 107
Tank 15 19 19 53
Airplane 42 41 42 125
Battleship 51 55 51 157
Total 144 148 150 442

Table 3. [in Chinese]

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Table 3. [in Chinese]

Method		Epochs	Launcher	Tank	Airplane	Battleship	mAP
Supervised transfer learning	Faster R-CNN	60	0.946	0.995	0.965	0.980	0.972
Supervised transfer learning	YOLO-v3	80	0.964	0.848	0.919	0.979	0.927
Semi-supervised transfer learning	Faster R-CNN	60	0.971	0.997	0.962	1.000	0.983
Semi-supervised transfer learning	YOLO-v3	80	1.000	0.973	0.936	1.000	0.975

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Weipeng Li, Xiaogang Yang, Chuanxiang Li, Ruitao Lu, Pan Huang. An improved semi-supervised transfer learning method for infrared object detection neural network[J]. Infrared and Laser Engineering, 2021, 50(3): 20200511

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