Table 2.

Performance of CNN models used for IARI intensity classification

Method	Accuracyavg (%)		Precisionavg		Recallavg		F1–scoreavg		Kappa		Time(s)
	w/o	w	w/o	w	w/o	w	w/o	w	w/o	w	w/o	w
AlexNet	92.6	92.8 ↑	0.905	0.897	0.955	0.965 ↑	0.929	0.930 ↑	0.919	0.921 ↑	0.019	0.006
VGG	92.4	90.5	0.903	0.872	0.942	0.984 ↑	0.922	0.925 ↑	0.917	0.896	0.070	0.091
ResNet	91.3	93.0 ↑	0.878	0.907 ↑	0.954	0.960 ↑	0.914	0.933 ↑	0.905	0.924 ↑	0.029	0.032
Inception	94.6	94.6	0.922	0.929 ↑	0.974	0.975 ↑	0.948	0.951 ↑	0.941	0.941	0.023	0.077
DenseNet	92.9	93.1 ↑	0.908	0.919 ↑	0.953	0.927	0.930	0.923	0.923	0.925 ↑	0.043	0.049
Ensemble Model	99.6	99.8 ↑	0.972	0.975 ↑	0.987	0.991 ↑	0.979	0.983 ↑	0.987	0.991 ↑	0.078	0.094

Note: w/o represents CNN without CBAM; w represents CBAM-CNN; Ensemble Model denotes the results of the proposed model; “↑” indicates that the result of CBAM-CNN is better than that of the original CNN