Comparison of cow face target detection algorithms based on deep learning

Guan Zhongbang; Yang Yanbo; Jing Jingyuan

doi:10.1117/12.3006127

10 October 2023 Comparison of cow face target detection algorithms based on deep learning

Guan Zhongbang, Yang Yanbo, Jing Jingyuan

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127991N (2023) https://doi.org/10.1117/12.3006127
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

To address issues in bull face detection, such as unsatisfactory inspection results and fragile inspection devices, we conducted a comparative study on existing representative deep network models (e.g. Mask R-CNN, YOLO, SSD, etc.) based on big data analysis discrepancy theory. The experimental results show that Mask R-CNN has the most efficient comprehensive analysis, although its detection rate is relatively slow. YOLO has a higher detection rate but less efficient comprehensive analysis compared to Mask R-CNN. SSD has the lowest comprehensive efficiency among the models.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guan Zhongbang, Yang Yanbo, and Jing Jingyuan "Comparison of cow face target detection algorithms based on deep learning", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127991N (10 October 2023); https://doi.org/10.1117/12.3006127

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