Wildlife image screening for infrared cameras based on YOLOv7

Qi Jiandong; Ma Zhongtian; Zheng Shangzi

doi:10.12171/j.1000-1522.20230112

Journal of Beijing Forestry University > 2024 > 46(2): 143-154. > DOI: 10.12171/j.1000-1522.20230112

Qi Jiandong, Ma Zhongtian, Zheng Shangzi. Wildlife image screening for infrared cameras based on YOLOv7[J]. Journal of Beijing Forestry University, 2024, 46(2): 143-154. DOI: 10.12171/j.1000-1522.20230112

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Wildlife image screening for infrared cameras based on YOLOv7

1.
School of Information, Beijing Forestry University, Beijing 100083, China
2.
Engineering Research Center for Forestry-Oriented Intelligent Information Processing ofNational Forestry and Grassland Administration, Beijing 100083, China

More Information

Received Date: May 11, 2023
Revised Date: December 25, 2023
Accepted Date: December 25, 2023
Available Online: December 27, 2023

Graphical Abstract

Abstract

Abstract

Objective
Due to the lush vegetation and disorderly trees in the wild environment, as well as the influence of factors such as environment, weather, and lighting, infrared cameras are prone to triggering shooting errors, resulting in the capture of a large amount of waste film, which requires a lot of manpower for screening. To solve such problems, based on the YOLOv7 model, this paper has made lightweight improvements to achieve automatic screening of waste pieces.
Method
This study constructed a dataset of 2 172 wildlife images collected from the Beijing Wuling Mountain Nature Reserve in the period of 2014−2015, and marked the positions of animals in the images. YOLOv7 network was improved in different ways. MicroBlock was introduced to replace the backbone network of YOLOv7, and the SPPCSPC structure was light-weighted to reduce the model parameters. SIoU loss, LNDown downsampling, and BiFPN were used to improve the model’s ability to detect animals. YOLOv5-m, YOLOv5-l, Ghost-YOLOv5-l, YOLOv6, YOLOX-M, and YOLOR-CSP models were trained on an Snapshot Serengeti camera trap subset dataset containing 10 000 images, and the screening effects of the model on wildlife images were compared. Transfer learning was used to train a self-built wildlife dataset, and the training effects of freezing different layers was tested.
Result
The improved model based on YOLOv7 reduced inference time by 14.3%, floating-point operations per second by 33.5%, and parameters by 17.8% compared with the YOLOv7 network. The error detection of the improved YOLOv7 model was also better than that of YOLOv7. Although the improved YOLOv7 did not achieve the best performance in all indicators compared with other models, it achieved a better balance between detection time and accuracy. In the self-built dataset, the unfrozen weight method had the best effect, and average precision was 12.6% higher than that of the model without transfer learning.
Conclusion
This study provides a faster and more accurate screening solution for wildlife monitoring networks in the Mountain area of Beijing Miyun.
- wildlife image,
- image filtering,
- deep learning,
- object detection

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References (19)

References

[1]	Kays R, McShea W J, Wikelski M. Born-digital biodiversity data: millions and billions[J]. Diversity and Distributions, 2020, 26(5): 644−648. doi: 10.1111/ddi.12993
[2]	肖治术, 肖文宏, 王天明, 等. 中国野生动物红外相机监测与研究: 现状及未来[J]. 生物多样性, 2022, 30: 234−259. Xiao Z S, Xiao W H, Wang T M, et al. Infrared camera monitoring and research on wildlife in China: current status and future[J]. Biodiversity, 2022, 30: 234−259.
[3]	Villa A G, Salazar A, Vargas F. Towards automatic wild animal monitoring: identification of animal species in camera-trap images using very deep convolutional neural networks[J]. Ecological Informatics, 2017, 41: 24−32. doi: 10.1016/j.ecoinf.2017.07.004
[4]	Norouzzadeh M S, Nguyen A, Kosmala M, et al. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning[J]. Proceedings of the National Academy of Sciences, 2018, 115(25): e5716−e5725.
[5]	Schneider S, Greenberg S, Taylor G W, et al. Three critical factors affecting automated image species recognition performance for camera traps[J]. Ecology and Evolution, 2020, 10(7): 3503−3517.
[6]	Guo Y, Rothfus T A, Ashour A S, et al. Varied channels region proposal and classification network for wildlife image classification under complex environment[J]. IET Image Processing, 2020, 14(4): 585−591. doi: 10.1049/iet-ipr.2019.1042
[7]	Chen P, Swarup P, Matkowski W M, et al. A study on giant panda recognition based on images of a large proportion of captive pandas[J]. Ecology and Evolution, 2020, 10(7): 3561−3573. doi: 10.1002/ece3.6152
[8]	Tan M, Chao W, Cheng J K, et al. Animal detection and classification from camera trap images using different mainstream object detection architectures[J]. Animals, 2022, 12(15): 1976. doi: 10.3390/ani12151976
[9]	Cai Z, Vasconcelos N. Cascade r-cnn: delving into high quality object detection[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake: IEEE, 2018: 6154−6162.
[10]	Tian Z, Shen C, Chen H, et al. Fcos: fully convolutional one-stage object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Seoul: IEEE, 2019: 9627−9636.
[11]	Vecvanags A, Aktas K, Pavlovs I, et al. Ungulate detection and species classification from camera trap images using RetinaNet and faster R-CNN[J]. Entropy, 2022, 24(3): 353. doi: 10.3390/e24030353
[12]	Roy A M, Bhaduri J, Kumar T, et al. WilDect-YOLO: an efficient and robust computer vision-based accurate object localization model for automated endangered wildlife detection[J]. Ecological Informatics, 2023, 75: 101919. doi: 10.1016/j.ecoinf.2022.101919
[13]	Huang G, Liu Z, van der Maaten L, et al. Densely connected convolutional networks[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Hawaii: IEEE, 2017: 4700−4708.
[14]	Li Y, Chen Y, Dai X, et al. Micronet: improving image recognition with extremely low flops[C]// Proceedings of the IEEE/CVF International Conference on Computer Vision. Montreal: IEEE, 2021: 468−477.
[15]	Tan M, Pang R, Le Q V. Efficientdet: scalable and efficient object detection[C]// Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. Seattle: IEEE, 2020: 10781−10790.
[16]	He K, Zhang X, Ren S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904−1916. doi: 10.1109/TPAMI.2015.2389824
[17]	Liu Z, Mao H, Wu C Y, et al. A convnet for the 2020s[C]// Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. New Orleans: IEEE, 2022: 11976−11986.
[18]	Ge Z, Liu S, Wang F, et al, Yolox: exceeding yolo series in 2021[J/OL]. arXiv preprint arXiv: 2107.08430, 2021 [2023−12−23]. https://arxiv.linfen3.top/pdf/2107.08430.pdf.
[19]	Wang C Y, Yeh I H, Liao H Y M. You only learn one representation: unified network for multiple tasks[J/OL]. arXiv preprint arXiv: 2105.04206. 2021 [2023−12−23]. https://arxiv.linfen3.top/abs/2105.04206.pdf.

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Wildlife image screening for infrared cameras based on YOLOv7

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