Identification and counting of dense bird populations based on end-to-end neural networks

Xinning Sui; Haifang Jian; Hongliang Li; Shuaikang Zheng; Hongchang Wang; Haiyan Ge; Dingkun Yu; Xuyan Zhou; Fengxin Dong

doi:10.1117/12.3031282

19 July 2024 Identification and counting of dense bird populations based on end-to-end neural networks

Xinning Sui, Haifang Jian, Hongliang Li, Shuaikang Zheng, Hongchang Wang, Haiyan Ge, Dingkun Yu, Xuyan Zhou, Fengxin Dong

Author Affiliations +

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131810P (2024) https://doi.org/10.1117/12.3031282
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

Currently, the identification and counting of birds in ecological monitoring are mainly done manually, which is a laborious and challenging process particularly when dealing with dense bird populations. In this paper, we propose an end-to-end multitasking neural network for identifying and counting dense bird populations. The network consists of three different branches, which respectively complete the tasks of population center position prediction, population boundary prediction, and counting. To achieve collaboration between multiple tasks and improve the performance of the model, a multitasking joint loss calculation strategy is proposed by utilizing the constraint relationships between multiple tasks. Additionally, we propose a method of population region mask, which can cope with the simultaneous emergence of multiple populations in actual scenarios. In order to validate the effectiveness of our method, we construct the first dense bird population identification and counting dataset and conduct thorough experiments. Compared to the baselines, our method has faster inference speed, lower resource consumption, and better identification and counting performance.

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

Citation Download Citation

Xinning Sui, Haifang Jian, Hongliang Li, Shuaikang Zheng, Hongchang Wang, Haiyan Ge, Dingkun Yu, Xuyan Zhou, and Fengxin Dong "Identification and counting of dense bird populations based on end-to-end neural networks", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131810P (19 July 2024); https://doi.org/10.1117/12.3031282

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