Journal of Ecology and Rural Environment ›› 2023, Vol. 39 ›› Issue (7): 918-923.doi: 10.19741/j.issn.1673-4831.2022.0615

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Analysis of the Accuracy of Artificial Intelligence Recognition and Artificial Recognition of Camera Traps Images: An Example of Macaca mulatta Monitoring on Dajinshan Island, Shanghai

LI Bi-cheng1, ZHANG Chen-xi2, JI Yu-xiang1, SUN Tan-feng3, DING Yi-min3, ZHANG Wei1, XIE Han-bin1, WANG Jun-fu1, ZHANG Yun-fei1, LI Xue-mei1, WANG Xiao-ming1, YANG Gang1,2   

  1. 1. Shanghai Science and Technology Museum/Laboratory of Ecological Security and Biodiversity Conservation of Yangtze River Delta Urban Agglomeration, Shanghai Science and Technology Museum, Shanghai 200127, China;
    2. College of Marine Science, Shanghai Ocean University, Shanghai 201306, China;
    3. School of Cyber Science and Engineering, Shanghai Jiaotong University, Shanghai 200240, China
  • Received:2022-06-17 Online:2023-07-25 Published:2023-07-19

Abstract: Artificial intelligence (AI) recognition of camera traps images has become one of the hot spots in the interdisciplinary research of ecology and AI. In order to explore the accuracy and influencing factors of artificial intelligence recognition of infrared camera animal images, the differences between artificial intelligence recognition and artificial recognition were compared. Taking the monitoring of macaques (Macaca mulatta) on Dajinshan Island in Shanghai as an example, the TOLO v3 model was applied for training and testing, and the feasibility of the TOLO v3 model to recognize a large number of infrared camera images was discussed. Meanwhile, the accuracy and recognition efficiency of AI image recognition and artificial recognition are compared to find out the optimal solution of recognition method under specific sample capacity. The recognition results of 11 106 photos show that the total recognition accuracy of artificial intelligence is 69.0%, and the average is 68.2%. The total accuracy of artificial recognition was 99.0%, and the average was 99.1%. The accuracy of artificial recognition was significantly higher than that of artificial intelligence (t=-9.256, df=22, P<0.01). The recognition accuracy of simple habitat background was significantly higher than that of complex habitat background (Z=-2.270, P=0.023). For artificial recognition, there was no significant difference in accuracy between simple habitat background and complex habitat background (Z=-0.406, P=0.685). AI recognition can be used for infrared images with a single habitat and background, but should be cautiously used for background recognition of complex habitats. In addition, AI recognition can be used for initial screening of large numbers of photographs. Artificial recognition can be used to identify photos with complex habitat backgrounds and to review photos after initial screening by artificial intelligence. For the sample size of ten thousand photos, artificial intelligence does not show obvious time advantage, but artificial recognition has an advantage of accuracy. With the continuous establishment and open application of various training data sets, artificial intelligence recognition for large vertebrates, especially for some well-known star species, may take the lead in replacing artificial recognition.

Key words: artificial intelligence, artificial recognition, accuracy, macaque, Dajinshan Island

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