YOLO-animal: an animal detection network based on improved YOLOv5 and attention mechanism

Si Li

doi:10.1117/12.3015658

29 January 2024 YOLO-animal: an animal detection network based on improved YOLOv5 and attention mechanism

Si Li

Proceedings Volume 12984, Fourth International Conference on Computer Vision and Information Technology (CVIT 2023); 1298402 (2024) https://doi.org/10.1117/12.3015658
Event: 2023 4th International Conference on Computer Vision and Information Technology (CVIT 2023), 2023, Beijing, China

Abstract

Animal detection and recognition is a crucial task in computer vision. YOLOv5 has been widely used for animal identification in the past few years. However, it is still a challenging task due to the diverse array of animal types found in complex environments. In this paper, we introduce a new attention mechanism based on the CBAM attention mechanism to enhance the performance of the network model. Specifically, the attention mechanism enhances the interplay between globally pooled channel information, thereby bolstering the ability to detect and recognize animals with similar features in complex backgrounds. Experimental results on the Oxford-IIIT Pet validation dataset demonstrate the effectiveness of the proposed model's robustness and its ability to perform effectively in real-world scenarios.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Si Li "YOLO-animal: an animal detection network based on improved YOLOv5 and attention mechanism", Proc. SPIE 12984, Fourth International Conference on Computer Vision and Information Technology (CVIT 2023), 1298402 (29 January 2024); https://doi.org/10.1117/12.3015658

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KEYWORDS

Animals

Object detection

Animal model studies

Positron emission tomography

Education and training

Computer vision technology

Deep learning

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