基于BS-ResNeXt-50的密云地区野生动物图像识别

doi:10.11707/j.1001-7488.LYKX20220378

Abstract

Abstract:

Objective: In the wild environment, the background of wildlife images captured by camera traps is complex, which poses a challenge for identifying wild animals in images with a large number of images and a wide variety of wildlife species. Based on convolutional neural network, this research aims to improve the existing structure and so as to implement the automatic recognition for wildlife images. Method: In this study, 2 712 wildlife images of 8 categories were taken from Wuling Mountain Beijing Nature Reserve, Miyun Districts, Beijing. The Auto Augment policy was randomly selected from 14 augmentation policies to add noise to the images. SENet and BlurPool were used to construct an improved network based on ResNeXt-50: SE-ResNeXt-50 for enhancement feature extraction, BP-ResNeXt-50 for Shift-invariance maintenance, and BS-ResNeXt-50 for both. The influences of fixed learning rate, segmented learning rate, and cosine annealing learning rate on the accuracy of the BS ResNeXt-50 network were tested on the self-built dataset. VGG16, ResNeXt-50, EfficientNet-B0, InceptionV3, DenseNet-121, and BS-ResNeXt-50 were used to train on 16 common categories of images in CCT public wildlife dataset, and the recognition accuracy of single species was compared.e influences of fixed learning rate, segmented learning rate, and cosine annealing learning rate on the accuracy of the BS ResNeXt-50 network were tested on the self-built dataset. VGG16, ResNeXt-50, EfficientNet-B0, InceptionV3, DenseNet-121, and BS-ResNeXt-50 were used to train on 16 common categories of images in CCT public wildlife dataset, and the recognition accuracy of single species was compared.eXt-50 is used to test influence of different learning rate include fixed and CosineAnnealing learning rate on collected dataset. VGG16, ResNeXt-50, EfficientNet-B0, InceptionV3, DenseNet-121, BS-ResNeXt-50 were used for training on CCT dataset, and the recognition accuracy of single species was compared. on ResNeXt-50: SE-ResNeXt-50 for enhancement feature extraction, BP-ResNeXt-50 for Shift-invariance maintenance, and BS-ResNeXt-50 for both. The influences of fixed learning rate, segmented learning rate, and cosine annealing learning rate on the accuracy of the BS ResNeXt-50 network were tested on the self-built dataset. VGG16, ResNeXt-50, EfficientNet-B0, InceptionV3, DenseNet-121, and BS-ResNeXt-50 were used to train on 16 common categories of images in CCT public wildlife dataset, and the recognition accuracy of single species was compared.eXt-50 is used to test influence of different learning rate include fixed and CosineAnnealing learning rate on collected dataset. VGG16, ResNeXt-50, EfficientNet-B0, InceptionV3, DenseNet-121, BS-ResNeXt-50 were used for training on CCT dataset, and the recognition accuracy of single species was compared. Result: The accuracy of SE-ResNeXt-50 and BP-ResNeXt-50 reached 75.16%±0.14% and 73.74%±0.13%, respectively. The enhanced scheme BS-ResNeXt-50, which integrated SENet and BlurPool, achieved an accuracy of 78.04%±0.11% when tested on a self built dataset, which was the best improved scheme. When the cosine annealing learning rate is used, the accuracy of BS-ResNeXt-50 was improved to 81.54%, which was 3.5% higher than that with the constant learning rate. The step decay learning rate achieved 79.3% accuracy, which was 2.24% less than the cosine annealing learning rate. The classification accuracy of BS-ResNeXt-50 was able to reach 95.07%, which was 1.95% higher than that of ResNeXt-50 on CCT dataset. At the same time, it was also 85.5% higher than that of VGG16, 91.38% higher than that of EfficientNet-B0, 91.38% higher than that of InceptionV3 and 93.3% higher than that of DenseNet-121. The prediction accuracy of each single category was also higher than that of the above model. In the recognition of a single category, except for the least one category, the accuracy of BS-ResNeXt-50 was 90% higher than that in other categories, and the highest category accuracy was 98.6%. Conclusion: The BS-ResNeXt-50 can more accurately complete the recognition task, and also has good generalization ability on different datasets.

Key words: wildlife images, species recognition, deep learning, convolutional neural network

CLC Number:

TP391.4

Jiandong Qi,Zhongtian Ma,Dehuai Zhang,Yun Tian. Wildlife Image Recognition in Miyun District Based on BS-ResNeXt-50[J]. Scientia Silvae Sinicae, 2023, 59(8): 112-122.

Figures/Tables 17

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物种 Species	图像数量 Image number
负鼠 Opossum	16479
郊狼 Coyote	16306
兔 Rabbit	12315
鹿 Deer	12191
浣熊 Raccoon	10888
鸟 Bird	9501
山猫 Bobcat	7597
猫 Cat	5163
松鼠 Squirrel	4430
啮齿动物（不含松鼠） Rodent (Squirrel exclusion)	4273
奶牛 Cow	3626
狗 Dog	3608
狐狸 Fox	2574
臭鼬 Skunk	1892

物种 Species	图像数量 Image number
狍 Roe Deer	500
鸟 Bird	500
山羊 Goat	332
松鼠 Squirrel	215
猪獾 Hog Badger	190
兔 Rabbit	155
豹猫 Leopard Cat	145
野猪 Boar	135

物种 Species	模型准确率Model accuracy（%）
物种 Species	ResNeXt-50	SE-ResNeXt-50	BP-ResNeXt-50	BS-ResNeXt-50
猪獾 Hog badger	65.2	68.9	67.4	73.2
鸟 Bird	81.5	84.7	85.3	88.7
野猪 Boar	66.4	71.3	67.1	72.5
豹猫 Leopard cat	63.7	65.4	63.7	70.2
狍 Roe deer	86.6	91.3	89.2	93.2
山羊 Goat	78.9	80.5	80.2	83.6
兔子 Rabbit	66.8	68.5	69.1	70.4
松鼠 Squirrel	66.2	70.7	67.9	72.5

物种 Species	模型准确率 Model accuracy（%）
物种 Species	VGG16	EfficientNet-B0	InceptionV3	DenseNet-121	ResNeXt-50	BS-ResNeXt-50
鸟 Bird	89.0	93.8	96.2	95.9	96.4	96.5
山猫 Bobcat	84.3	87.3	88.8	91.2	91.0	95.0
猫 Cat	86.8	92.5	93.2	95.6	94.0	97.6
奶牛 Cow	92.3	95.5	96.5	96.7	97.3	97.3
郊狼 Coyote	86.4	91.4	92.5	93.7	92.3	95.5
鹿 Deer	90.6	95.2	98.1	96.3	98.0	98.6
狗 Dog	80.1	88.5	90.2	93.0	92.9	93.3
狐狸 Fox	85.5	89.1	90.7	90.2	88.7	92.8
负鼠 Opossum	90.2	86.4	85.7	94.9	93.0	96.1
兔 Rabbit	83.0	90.4	92.3	93.7	93.2	94.5
浣熊 Raccoon	85.7	91.2	90.2	92.8	92.5	95.0
啮齿动物（不含松鼠） Rodent (Squirrel exclusion)	85.9	88.2	90.5	94.8	94.3	95.6
臭鼬 Skunk	78.8	83.3	84.0	85.0	86.9	88.9
松鼠 Squirrel	78.4	90.5	90.5	93.1	93.2	94.4

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Wildlife Image Recognition in Miyun District Based on BS-ResNeXt-50

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