基于Wasserstein距离和相关对齐迁移学习的野生动物图像识别方法

doi:10.11707/j.1001-7488.LYKX20230399

Abstract

Abstract:

Objective: This study aims to address the influence of complex factors such as lighting, background, and shooting scale on the accuracy of wildlife image recognition. Method: In this study, the wild animal images captured by infrared triggered cameras in the wild were used as the object: 1) Two publicly available wildlife datasets, ENA24 and NACTI, were used to construct disjoint datasets S1 and S2, comprising a total of 11 animal categories and 25 591 images. 2) To tackle domain shift issues, a ResNet50 network was utilized as a feature extraction module to build a domain adversarial network, effectively alleviating domain bias. 3) A representation learning network incorporating Wasserstein distance and correlation alignment was proposed to establish a transfer learning network for feature extraction and recognition, so as to further exploit transferable features. Result: The performance of different models in wildlife recognition was evaluated using the average accuracy metric. Results indicated that the average accuracy on 11 wildlife categories for eight models, namely ResNet50, DDC, DCORAL, DAN, DANN, CDAN, HAN, and JTN, was 48.4%, 51.6%, 49.6%, 52.6%, 45.2%, 50.9%, 54.6%, and 53.5%, respectively. Upon enhancing the ResNet50 base model with improved residual modules and introducing a representation learning network incorporating Wasserstein distance and correlation alignment, the average accuracy for 11 wildlife categories was improved by 2.7% compared to the existing best result with the comparative methods. Conclusion: The transfer learning method based on Wasserstein distance and correlation alignment has achieved an average accuracy of 57.3% in wildlife recognition. The introduction of representation learning based on Wasserstein distance and correlation alignment can effectively improve the accuracy of the wildlife recognition model.

Key words: wild animal, Wasserstein distance, correlation alignment, transfer learning, image recognition

CLC Number:

TP391.4

Changchun Zhang,Dafang Li,Junguo Zhang. Wildlife Images Recognition Method Based on Wasserstein Distance and Correlation Alignment Transfer Learning[J]. Scientia Silvae Sinicae, 2024, 60(8): 25-32.

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References 0

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数据集 Datasets	熊 Bear	短尾猫 Bobcat	丛林狼 Coyote	鹿 Deer	狐狸 Fox	浣熊 Raccoon	松鼠 Squirrel	臭鼬 Striped skunk	负鼠 Virginia opossum	野猪 Wild boar	火鸡 Wild turkey
S1	2870	455	1814	4336	1866	1376	1386	1323	110	1893	815
S2	867	328	181	2357	427	142	927	295	715	819	289

模型 Models	迁移任务S1→S2 Transfer task S1→S2	迁移任务S2→S1 Transfer task S2→S1	平均准确率 Average accuracy(% )
ResNet50	41.4	55.4	48.4
DDC	44.0	59.2	51.6
DCORAL	44.1	55.1	49.6
DANN	40.8	49.6	45.2
CDAN	45.5	56.2	50.9
DAN	44.7	60.5	52.6
HAN	47.2	62.0	54.6
JTN	44.4	62.5	53.5
WDCA-TLNet	48.4	66.2	57.3

模型 Models	迁移任务S1→S2 Transfer task S1→S2	迁移任务S2→S1 Transfer task S2→S1	平均准确率 Average accuracy
VGG16	42.0	55.0	48.5
DDC	33.8	51.7	42.8
DCORAL	40.8	53.3	47.1
CDAN	43.8	56.5	50.2
JTN	43.5	57.3	50.4
WDCA-TLNet	44.9	58.3	51.6

模型 Models	迁移任务S1→S2 Transfer task S1→S2	迁移任务 S2→S1 Transfer ask S2→S1	平均准确率 Average accuracy（%）
ResNet50	41.4	55.4	48.4
WDCA-TLNet w/o CAL	42.4	58.5	50.5
WDCA-TLNet w/o DAL	44.0	58.4	51.2
WDCA-TLNet w/o WD	46.2	61.0	53.6
WDCA-TLNet	48.4	66.2	57.3

模型 Models	平均准确率 Average accuracy（%）	精确率 Precision	召回率 Recall	F1值
DCORAL	44.1	0.38	0.29	0.24
JTN	44.4	0.38	0.37	0.32
WDCA-TLNet	48.4	0.43	0.40	0.35

Wildlife Images Recognition Method Based on Wasserstein Distance and Correlation Alignment Transfer Learning

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