A livestock recognition and detection algorithm based on improved YOLOv5s

Jiajun Niu; Chunmei Li; Chengwu Fang

doi:10.1117/12.3044111

29 August 2024 A livestock recognition and detection algorithm based on improved YOLOv5s

Jiajun Niu, Chunmei Li, Chengwu Fang

Proceedings Volume 13249, International Conference on Computer Vision, Robotics, and Automation Engineering (CRAE 2024); 1324913 (2024) https://doi.org/10.1117/12.3044111
Event: 2024 International Conference on Computer Vision, Robotics and Automation Engineering, 2024, Kunming, China

Abstract

As the largest natural reserve in China, the Three-River-Source Region possesses abundant grassland resources. However, in recent years, the grasslands in this area have been facing severe degradation issues. Overgrazing by livestock, mainly cattle and sheep, who are the primary livestock of the local herdsmen, has led to excessive grassland depletion. Addressing this phenomenon, this paper aims to provide technical support for grassland workers to explore the relationship between livestock grazing and grassland degradation by accurately identifying the number of cattle and sheep. This will enable the formulation of more effective grazing management strategies. This study involved field data collection and manual annotation to construct a dataset comprising 10,000 images of cattle and sheep. Building upon the original YOLOv5 model, optimizations were made by integrating compression techniques and the Squeeze-and-Excitation (SE) module, as well as enhancing the small object detection stage. Following these optimizations, the model achieved performance improvements of 1.2% in precision, 4.03% in recall, and 3.6% in mAP0.5 (mean average precision at an IoU threshold of 0.5) compared to the original model. These enhancements provide efficient and accurate technical means for livestock counting in the Three-River-Source Region.

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

Citation Download Citation

Jiajun Niu, Chunmei Li, and Chengwu Fang "A livestock recognition and detection algorithm based on improved YOLOv5s", Proc. SPIE 13249, International Conference on Computer Vision, Robotics, and Automation Engineering (CRAE 2024), 1324913 (29 August 2024); https://doi.org/10.1117/12.3044111

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KEYWORDS

Object detection

Animal model studies

Detection and tracking algorithms

Mathematical optimization

Ablation

Data modeling

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