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Xu N, Ma Z, Xia Y, Dong Y, Zi J, Xu D, Xu F, Su X, Zhang H, Chen F. A Serial Multi-Scale Feature Fusion and Enhancement Network for Amur Tiger Re-Identification. Animals (Basel) 2024; 14:1106. [PMID: 38612345 PMCID: PMC11011027 DOI: 10.3390/ani14071106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 03/26/2024] [Accepted: 04/02/2024] [Indexed: 04/14/2024] Open
Abstract
The Amur tiger is an important endangered species in the world, and its re-identification (re-ID) plays an important role in regional biodiversity assessment and wildlife resource statistics. This paper focuses on the task of Amur tiger re-ID based on visible light images from screenshots of surveillance videos or camera traps, aiming to solve the problem of low accuracy caused by camera perspective, noisy background noise, changes in motion posture, and deformation of Amur tiger body patterns during the re-ID process. To overcome this challenge, we propose a serial multi-scale feature fusion and enhancement re-ID network of Amur tiger for this task, in which global and local branches are constructed. Specifically, we design a global inverted pyramid multi-scale feature fusion method in the global branch to effectively fuse multi-scale global features and achieve high-level, fine-grained, and deep semantic feature preservation. We also design a local dual-domain attention feature enhancement method in the local branch, further enhancing local feature extraction and fusion by dividing local feature blocks. Based on the above model structure, we evaluated the effectiveness and feasibility of the model on the public dataset of the Amur Tiger Re-identification in the Wild (ATRW), and achieved good results on mAP, Rank-1, and Rank-5, demonstrating a certain competitiveness. In addition, since our proposed model does not require the introduction of additional expensive annotation information and does not incorporate other pre-training modules, it has important advantages such as strong transferability and simple training.
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Affiliation(s)
- Nuo Xu
- School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China; (N.X.); (Z.M.); (Y.X.); (Y.D.); (J.Z.); (D.X.); (F.X.); (X.S.); (H.Z.)
| | - Zhibin Ma
- School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China; (N.X.); (Z.M.); (Y.X.); (Y.D.); (J.Z.); (D.X.); (F.X.); (X.S.); (H.Z.)
| | - Yi Xia
- School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China; (N.X.); (Z.M.); (Y.X.); (Y.D.); (J.Z.); (D.X.); (F.X.); (X.S.); (H.Z.)
| | - Yanqi Dong
- School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China; (N.X.); (Z.M.); (Y.X.); (Y.D.); (J.Z.); (D.X.); (F.X.); (X.S.); (H.Z.)
| | - Jiali Zi
- School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China; (N.X.); (Z.M.); (Y.X.); (Y.D.); (J.Z.); (D.X.); (F.X.); (X.S.); (H.Z.)
| | - Delong Xu
- School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China; (N.X.); (Z.M.); (Y.X.); (Y.D.); (J.Z.); (D.X.); (F.X.); (X.S.); (H.Z.)
| | - Fu Xu
- School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China; (N.X.); (Z.M.); (Y.X.); (Y.D.); (J.Z.); (D.X.); (F.X.); (X.S.); (H.Z.)
- Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China
| | - Xiaohui Su
- School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China; (N.X.); (Z.M.); (Y.X.); (Y.D.); (J.Z.); (D.X.); (F.X.); (X.S.); (H.Z.)
- Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China
| | - Haiyan Zhang
- School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China; (N.X.); (Z.M.); (Y.X.); (Y.D.); (J.Z.); (D.X.); (F.X.); (X.S.); (H.Z.)
- Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China
| | - Feixiang Chen
- School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China; (N.X.); (Z.M.); (Y.X.); (Y.D.); (J.Z.); (D.X.); (F.X.); (X.S.); (H.Z.)
- Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China
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Hernández-López R, Travieso-González CM. Reptile Identification for Endemic and Invasive Alien Species Using Transfer Learning Approaches. SENSORS (BASEL, SWITZERLAND) 2024; 24:1372. [PMID: 38474908 DOI: 10.3390/s24051372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/29/2024] [Accepted: 02/17/2024] [Indexed: 03/14/2024]
Abstract
The Canary Islands are considered a hotspot of biodiversity and have high levels of endemicity, including endemic reptile species. Nowadays, some invasive alien species of reptiles are proliferating with no control in different parts of the territory, creating a dangerous situation for the ecosystems of this archipelago. Despite the fact that the regional authorities have initiated actions to try to control the proliferation of invasive species, the problem has not been solved as it depends on sporadic sightings, and it is impossible to determine when these species appear. Since no studies for automatically identifying certain species of reptiles endemic to the Canary Islands have been found in the current state-of-the-art, from the Signals and Communications Department of the Las Palmas de Gran Canaria University (ULPGC), we consider the possibility of developing a detection system based on automatic species recognition using deep learning (DL) techniques. So this research conducts an initial identification study of some species of interest by implementing different neural network models based on transfer learning approaches. This study concludes with a comparison in which the best performance is achieved by integrating the EfficientNetV2B3 base model, which has a mean Accuracy of 98.75%.
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Affiliation(s)
- Ruymán Hernández-López
- Signals and Communications Department (DSC), Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain
| | - Carlos M Travieso-González
- Signals and Communications Department (DSC), Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain
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Zhang YJ, Luo Z, Sun Y, Liu J, Chen Z. From beasts to bytes: Revolutionizing zoological research with artificial intelligence. Zool Res 2023; 44:1115-1131. [PMID: 37933101 PMCID: PMC10802096 DOI: 10.24272/j.issn.2095-8137.2023.263] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 10/30/2023] [Indexed: 11/08/2023] Open
Abstract
Since the late 2010s, Artificial Intelligence (AI) including machine learning, boosted through deep learning, has boomed as a vital tool to leverage computer vision, natural language processing and speech recognition in revolutionizing zoological research. This review provides an overview of the primary tasks, core models, datasets, and applications of AI in zoological research, including animal classification, resource conservation, behavior, development, genetics and evolution, breeding and health, disease models, and paleontology. Additionally, we explore the challenges and future directions of integrating AI into this field. Based on numerous case studies, this review outlines various avenues for incorporating AI into zoological research and underscores its potential to enhance our understanding of the intricate relationships that exist within the animal kingdom. As we build a bridge between beast and byte realms, this review serves as a resource for envisioning novel AI applications in zoological research that have not yet been explored.
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Affiliation(s)
- Yu-Juan Zhang
- Chongqing Key Laboratory of Vector Insects
- Chongqing Key Laboratory of Animal Biology
- College of Life Science, Chongqing Normal University, Chongqing 401331, China
| | - Zeyu Luo
- Chongqing Key Laboratory of Vector Insects
- Chongqing Key Laboratory of Animal Biology
- College of Life Science, Chongqing Normal University, Chongqing 401331, China
| | - Yawen Sun
- Chongqing Key Laboratory of Vector Insects
- Chongqing Key Laboratory of Animal Biology
- College of Life Science, Chongqing Normal University, Chongqing 401331, China
| | - Junhao Liu
- Chongqing Key Laboratory of Vector Insects
- Chongqing Key Laboratory of Animal Biology
- College of Life Science, Chongqing Normal University, Chongqing 401331, China
| | - Zongqing Chen
- School of Mathematical Sciences
- National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing 401331, China. E-mail:
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Mou C, Liang A, Hu C, Meng F, Han B, Xu F. Monitoring Endangered and Rare Wildlife in the Field: A Foundation Deep Learning Model Integrating Human Knowledge for Incremental Recognition with Few Data and Low Cost. Animals (Basel) 2023; 13:3168. [PMID: 37893892 PMCID: PMC10603653 DOI: 10.3390/ani13203168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 10/04/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023] Open
Abstract
Intelligent monitoring of endangered and rare wildlife is important for biodiversity conservation. In practical monitoring, few animal data are available to train recognition algorithms. The system must, therefore, achieve high accuracy with limited resources. Simultaneously, zoologists expect the system to be able to discover unknown species to make significant discoveries. To date, none of the current algorithms have these abilities. Therefore, this paper proposed a KI-CLIP method. Firstly, by first introducing CLIP, a foundation deep learning model that has not yet been applied in animal fields, the powerful recognition capability with few training resources is exploited with an additional shallow network. Secondly, inspired by the single-image recognition abilities of zoologists, we incorporate easily accessible expert description texts to improve performance with few samples. Finally, a simple incremental learning module is designed to detect unknown species. We conducted extensive comparative experiments, ablation experiments, and case studies on 12 datasets containing real data. The results validate the effectiveness of KI-CLIP, which can be trained on multiple real scenarios in seconds, achieving in our study over 90% recognition accuracy with only 8 training samples, and over 97% with 16 training samples. In conclusion, KI-CLIP is suitable for practical animal monitoring.
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Affiliation(s)
- Chao Mou
- School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China; (C.M.)
- Engineering Research Center for Forestry-oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing 100083, China
| | - Aokang Liang
- School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China; (C.M.)
- Engineering Research Center for Forestry-oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing 100083, China
| | - Chunying Hu
- School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China; (C.M.)
- Engineering Research Center for Forestry-oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing 100083, China
| | - Fanyu Meng
- School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China; (C.M.)
- Engineering Research Center for Forestry-oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing 100083, China
| | - Baixun Han
- School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China; (C.M.)
- Engineering Research Center for Forestry-oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing 100083, China
| | - Fu Xu
- School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China; (C.M.)
- Engineering Research Center for Forestry-oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing 100083, China
- State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China
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