Deep Neural Networks Based Multiclass Animal Detection and Classification in Drone Imagery

Chen, Changrong; Edirisinghe, E.A.; Leonce, Andrew; Simkins, Gregory; Khafaga, Tamer; Sher Shah, Moayyed; Yahya, Umar

Abstract

There is a growing interest among the research community in the search for possible technology-driven strategies for the conservation of the much-needed, historically rich and culturally important, desert life. In this work, we investigate the use of one of the best available Deep Neural Networks, YOLO Version-5 (v5), to enable offline detection, identification and classification of three popular desert animals (i.e Camels, Oryxes, and Gazelles) in a Drone Imagery Dataset captured by the Dubai Desert Conservation Reserve (DDCR), United Arab Emirates. The dataset contains over 1200 images, which were partitioned into training, validation, and testing data sub-sets in a 8:1:1 ratio, respectively. We trained three multi-class models, animal classification models, based on YOLO v5 Small(S), Medium(M) and Large(L), representing increasingly deep and complex architectures, to simultaneously detect and label the 3 kinds of animals. Models' performance was compared on the basis of classification accuracy (F1-Measure), The multi-class detector models generated were also compared with the single animal detector models created using the same network architectures, to assess the trained network's robustness against detecting more than one class of object. YOLO v5 L achieved the highest multi-class average classification accuracy of 96.71 percent (95.39 - 98.98). In comparison with the single animal detector models, the multi-class models exhibited the ability to correctly detect the target objects even for cases where the objects are located close to each other. We show that the promising results achieved in this work provide a promising foundation for the development of real-time multiclass identification and classification applications utilizing UAV imagery, to aid in the conservation efforts of fauna, particularly in the urbanized modern-day deserts and semi-desert places, such as the DDCR. We provide comprehensive test results and an analysis of results to demonstrate the effectiveness of the proposed models.

Citation

Chen, C., Edirisinghe, E., Leonce, A., Simkins, G., Khafaga, T., Sher Shah, M., & Yahya, U. (2023, October). Deep Neural Networks Based Multiclass Animal Detection and Classification in Drone Imagery. Presented at 2023 International Symposium on Networks, Computers and Communications (ISNCC), Doha, Qatar

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2023 International Symposium on Networks, Computers and Communications (ISNCC)
• Start Date: Oct 23, 2023
• End Date: Oct 26, 2023
• Acceptance Date: Oct 23, 2023
• Publication Date: Oct 23, 2023
• Deposit Date: Dec 11, 2023
• Publicly Available Date: Dec 11, 2023
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Book Title: 2023 International Symposium on Networks, Computers and Communications (ISNCC)
• ISBN: 979-8-3503-3560-6
• DOI: https://doi.org/10.1109/isncc58260.2023.10323685
• Public URL: https://keele-repository.worktribe.com/output/660955