Research on the detection of safflower filaments in natural scenarios based on deep learning algorithms

Bangbang Chen; Baojian Ma; Xiangdong Liu; Di Yan

doi:10.1117/12.3036156

21 October 2024 Research on the detection of safflower filaments in natural scenarios based on deep learning algorithms

Bangbang Chen, Baojian Ma, Xiangdong Liu, Di Yan

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Proceedings Volume 13401, International Conference on Automation and Intelligent Technology (ICAIT 2024); 1340107 (2024) https://doi.org/10.1117/12.3036156
Event: 2024 International Conference on Automation and Intelligent Technology (ICAIT 2024), 2024, Wuhan, China

Abstract

In response to the challenges posed by high labor costs and low mechanization in the harvesting of safflower filaments in Xinjiang, this study introduces an intelligent detection method leveraging YOLOv8s. A safflower filament dataset was developed and enhanced as the basis for constructing a detection model that incorporates the C2f, SPPF, and Detect modules, and the Loss function. The model was evaluated using recall rate (R), precision (P), and mean average precision (mAP) as metrics. We compared 12 variants of target algorithms based on YOLOv3, YOLOv5, YOLOv6, and YOLOv8. The findings indicate that YOLOv8s achieved a precision of 82.8%, a recall of 78.2%, and mAP of 86.2%. Relative to YOLOv3-tiny and YOLOv5s, YOLOv8s demonstrated higher recall and mAP. Despite its compact size of only 5.96MB, YOLOv8s exhibited superior confidence with no missed or false detections compared to these models. To further affirm the reliability of YOLOv8s, its detection performance on safflower filaments was tested under various conditions, achieving mAP values of 91.8%, 92.8%, 90.3%, 79%, and 92.5% respectively, showcasing its rapid and accurate detection capabilities while maintaining lightness and robustness, potentially serving as a technical reference for the development of intelligent safflower harvesting robots.

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

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Bangbang Chen, Baojian Ma, Xiangdong Liu, and Di Yan "Research on the detection of safflower filaments in natural scenarios based on deep learning algorithms", Proc. SPIE 13401, International Conference on Automation and Intelligent Technology (ICAIT 2024), 1340107 (21 October 2024); https://doi.org/10.1117/12.3036156

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Object detection

Detection and tracking algorithms

Performance modeling

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Education and training

Deep learning

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