改进ConvNeXt网络的树种识别方法

doi:10.11707/j.1001-7488.LYKX20240121

Abstract

Abstract:

Objective: In this study, an improved tree species recognition model of ConvNeXt network was proposed by using a transfer learning strategy and introducing the SimAM attention module and ECA channel attention mechanism, so as to improve the efficiency and accuracy of tree species recognition work and solve the difficulties encountered in the recognition work. Method: The bark images of common 12 tree species were used as the research object, and the data were expanded by traditional data enhancement methods to prevent model overfitting. An improved ConvNeXt-based network was constructed using SimAM and ECA channel attention mechanisms: SA-ConvNeXt for enhanced feature extraction, E-ConvNeXt for enhanced weighting of important features, and ES-ConvNeXt combining the two. The effect of the dataset on the accuracy of the ES-ConvNeXt network before and after enhancement was tested. The recognition effects with the ES-ConvNeXt model were compared by using the Resnet34, Rennet50, GoogLeNet, Swin Transformer, Densenet121, and ConvNeXt networks. Result: SA-ConvNeXt and E-ConvNeXt achieved 95.14%±0.42% and 96.085%±0.235% accuracy, respectively. ES-ConvNeXt, which incorporates SimAm and ECA attention modules, achieved an accuracy of 97.445%±0.635% for the test on the augmented dataset, its recognition accuracy for a single tree species exceeded 93%, and the highest category accuracy reached 99.79%, making it the optimal solution. The model trained with expanded data had optimal accuracy and loss values for the validation set both in terms of speed of convergence and final stabilized values compared to the model trained using the original data. With the same dataset, the recognition accuracies using Resnet34, Rennet50, GoogLeNet, Swin Transformer, Densenet121, and ConvNeXt networks were 92.74%, 94.47%, 90.52%, 92.85%, 70.38%, and 94.72%, respectively, which were all lower than the 97.81% obtained by the new improved model (ES-ConvNeXt model), further illustrating the effectiveness of the improved ES-ConvNeXt model. Conclusion: Data enhancement is effective for model accuracy improvement, and on the data-enhanced dataset, the improved ES-ConvNeXt model can perform the tree classification task more accurately compared to the other models, and it also has better generalization ability on different tree species.

Key words: tree species recognition, ConvNeXt, SimAM attention mechanism, ECA channel attention mechanism

CLC Number:

TP391.4

Bingbing Yang,Jie Xu. Tree Species Recognition Based on Improved ConvNeXt Network[J]. Scientia Silvae Sinicae, 2025, 61(2): 31-39.

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结构 Structure	输入 Input	卷积核及步距 Convolution kernel and step size	输出 Output
卷积层：ES-Block层1 Convolution layer： ES-Block layer 1	224×224×3	4×4，s4	56×56×96
	56×56×96	d7×7，s1	56×56×96
	56×56×96	1×1，s1	56×56×384
	56×56×384	1×1，s1	56×56×96
下采样：ES-Block层2 Downsampling： ES-Block layer 1	56×56×96	2×2，s2	28×28×192
	56×56×192	d7×7，s1	28×28×192
	28×28×192	1×1，s1	28×28×768
	28×28×768	1×1，s1	28×28×192
下采样：ES-Block层3 Downsampling： ES-Block layer 3	28×28×192	2×2，s2	14×14×384
	14×14×384	d7×7，s1	14×14×384
	14×14×384	1×1，s1	14×14×1536
	14×14×1536	1×1，s1	14×14×384
下采样：ES-Block层4 Downsampling： ES-Block layer 4	14×14×384	2×2，s2	7×7×768
	7×7×768	d7×7，s1	7×7×768
	7×7×768	1×1，s1	7×7×3072
	7×7×3072	1×1，s1	7×7×768

物种 Species	模型准确率Model accuracy（%）
物种 Species	ConvNeXt	SA-ConvNeXt	E-ConvNeXt	ES-ConvNeXt
梣叶槭Acer negundo	82.75	93.15	94.46	95.72
枫香树Liquidambar formosana	92.48	95.27	95.88	97.31
洋槐树Robinia pseudoacacia	92.44	97.25	96.21	97.47
银杏Ginkgo biloba	80.77	94.58	95.64	97.72
复羽叶栾树Koelreuteria bipinnata	87.61	94.80	93.51	96.84
家榆Ulmus pumila	89.72	95.90	96.29	95.96
女贞Ligustrum lucidum	88.38	93.44	93.92	93.36
山杨Populus davidiana	99.36	99.79	100.00	99.79
水曲柳Fraxinus mandshurica	90.70	91.94	94.56	96.63
乌桕Sapium sebiferum	82.85	93.53	95.44	97.47
梧桐Firmiana simplex	99.58	99.38	99.17	99.58
喜树Camptotheca acuminata	88.16	94.85	95.17	97.27

数据集 Database	准确率 Accuracy	精确率 Precision	召回率 Recall	特异性 Specificity
原始数据 Original data	96.76	96.23	96.07	99.71
增强后数据 After data enhancement	97.81	97.82	97.81	99.80

模型 Model	准确率 Accuracy	精确率 Precision	召回率 Recall	特异性 Specificity
Swin-T	92.85	92.91	92.86	99.36
ResNet34	92.74	92.84	92.73	99.34
ResNet50	94.47	94.53	94.48	99.50
GoogLeNet	90.52	90.53	90.52	99.14
DenseNet121	70.38	70.98	70.40	97.54
ConvNeXt	94.72	94.74	94.72	99.53
ES-ConvNeXt	97.81	97.82	97.81	99.80

Tree Species Recognition Based on Improved ConvNeXt Network

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