Photovoltaic hot spot detection method incorporating knowledge distillation and attention mechanisms

Table 1. Configuration of Hardware and software
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Table 1. Configuration of Hardware and software
Configuration name Version parameter
Operating system Microsoft Windows 10
CPU Inter Core i7-11700 @2.50 GHz
GPU NVIDIA GeForce RTX 3070
CUDA 11.6
Deep Learning Framework Pytorch

Table 2. Different module action experiments

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Table 2. Different module action experiments

FCAHSI	CCLA	函数优化	M_P/%	M_R/%	mAP@0.5%	Mt/ms	Parameters	GFLOPs
			73.0	67.1	70.6	4.5	7 012 822	15.8
√			87.4	80.8	86.5	6.9	12 673 206	29.8
	√		86.3	78.4	85.9	5.8	10 204 375	26.1
√		√	89.1	81.1	87.5	6.9	12 673 206	29.8
	√	√	89.8	80.1	86.8	6.4	10 204 375	26.1
√	√	√	90.2	79.4	87.6	7.7	12 731 191	29.9

Table 3. Result of distillation experiment
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Table 3. Result of distillation experiment
方法 M_P/% M_R/% mAP@0.5% Mt/ms Parameters GFLOPs
教师网络 90.2 79.4 87.6 7.7 12 731 191 29.9
学生网络 73.0 67.1 70.6 4.5 7 012 822 15.8
Ours 84.3 79.9 84.8 4.3 7 012 822 15.8

Table 4. Comparison of detection results of different algorithms

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Table 4. Comparison of detection results of different algorithms

指标方法	M_P/%	M_R/%	mAP%	Mt/ms
SSD	89.5	46.7	76.0	29.7
CenterNet	78.2	33.9	54.4	13.4
EfficientDet	85.6	20.9	32.4	35.0
Retinanet	90.9	39.6	42.4	25.2
FCOS	83.1	42.5	63.0	27.6
Yolov4	76.1	30.8	44.2	32.7
Yolov5	73.0	67.1	70.6	4.8
Yolov7	74.4	71.3	77.5	11.1
Ours	84.3	79.9	84.8	4.3

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Shuai HAO, Yingqi WU, Xu MA, Tong LI, Haiying WANG. Photovoltaic hot spot detection method incorporating knowledge distillation and attention mechanisms[J]. Optics and Precision Engineering, 2023, 31(24): 3640

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