Apple surface defect detection algorithm based on improved YOLOv8

Tao Liu; Xiaohui Huang

doi:10.1117/12.3051665

18 November 2024 Apple surface defect detection algorithm based on improved YOLOv8

Tao Liu, Xiaohui Huang

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134030V (2024) https://doi.org/10.1117/12.3051665
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

To address the issue of low detection accuracy for surface defects in non-destructive apple inspection, a YOLOv8-AP algorithm for apple surface defect detection is proposed. Firstly, the Adown downsampling module is introduced into the feature extraction network to replace the traditional downsampling operation, thereby enhancing the model's feature extraction capabilities. Secondly, the FHLA structure is constructed in the neck network to integrate information from the backbone network and the feature pyramid, increasing the amount of feature information and enhancing the model's ability to detect multi-scale variations of apple surface defects. Lastly, LSD-DECD is proposed to improve the model's performance in localization and classification. Experimental results show that YOLOv8-AP achieves an mAP50 of 85.5% and an mAP50-90 of 52.9% for apple surface defect detection. Compared to the YOLOv8 algorithm, these values represent improvements of 2.6% and 1.4%, respectively. The improved YOLOv8-AP algorithm enhances the accuracy of the YOLOv8 algorithm, enabling precise recognition of apple surface defects.

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

Citation Download Citation

Tao Liu and Xiaohui Huang "Apple surface defect detection algorithm based on improved YOLOv8", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134030V (18 November 2024); https://doi.org/10.1117/12.3051665

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KEYWORDS

Object detection

Detection and tracking algorithms

Defect detection

Feature extraction

Performance modeling

Feature fusion

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