Apple recognition method based on the improved YOLOv8 model

Huatao Song; Gengyang Song; Xia Dong; Haibo Xu

doi:10.1117/12.3040996

11 September 2024 Apple recognition method based on the improved YOLOv8 model

Huatao Song, Gengyang Song, Xia Dong, Haibo Xu

Proceedings Volume 13253, Fourth International Conference on Signal Image Processing and Communication (ICSIPC 2024); 132530Y (2024) https://doi.org/10.1117/12.3040996
Event: 4th International Conference on Signal Image Processing and Communication, 2024, Xi'an, China

Abstract

For apple recognition in natural orchard environments, the YOLOv8 algorithm offers unique advantages of accurate detection and rapid speed. This paper introduces an enhanced YOLOv8 model derived from the original YOLOv8 model. Firstly, the CReToNeXt structure replaces the C2f module in the original YOLOv8 model's head section, reducing computational complexity and increasing the model's receptive field, thereby enhancing detector performance by fully exploiting and utilizing multi-scale information of features to improve object detection accuracy. Then, the Shuffle- Attention (SA) attention mechanism module is introduced, enabling the algorithm to integrate deeper features with larger receptive fields, reducing the impact of imbalanced training sample annotation quality, improving the precision of predicted bounding boxes, and enhancing the detection ability of small objects. The study in this paper demonstrates that the improved YOLOv8 model attains a distinct enhancement over the original model: Precision (P) increases by 1.1%, Recall (R) increases by 0.9%, and mean Average Precision (mAP) increases by 1.5%.

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

Citation Download Citation

Huatao Song, Gengyang Song, Xia Dong, and Haibo Xu "Apple recognition method based on the improved YOLOv8 model", Proc. SPIE 13253, Fourth International Conference on Signal Image Processing and Communication (ICSIPC 2024), 132530Y (11 September 2024); https://doi.org/10.1117/12.3040996

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KEYWORDS

Object detection

Detection and tracking algorithms

Network architectures

Robots

Education and training

Feature extraction

Deep learning

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