Green pepper recognition algorithm based on improved YOLOv7

Fanbo Xiao; Dejin Zhao

doi:10.1117/12.3048065

3 October 2024 Green pepper recognition algorithm based on improved YOLOv7

Fanbo Xiao, Dejin Zhao

Author Affiliations +

Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 132720G (2024) https://doi.org/10.1117/12.3048065
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China

Abstract

In response to the problem of low recognition accuracy for green pepper picking in intelligent picking scenarios, an enhanced model based on the YOLOv7 object detection algorithm was proposed to elevate the recognition accuracy of green peppers. Firstly, the Self Attention mechanism is introduced to focus on the correlation between different positions, thereby capturing richer spatial and channel information of contextual information and reducing interference from other targets. Secondly, ASFF feature fusion module is added to adaptively integrate multi-scale features by learning weights to improve target recognition ability. Through training experiments on the established dataset, according to the test results, the evaluation accuracy rate (mAP) of the improved YOLOV7 model is 92.7%, which is an increase of 3.2% compared with the original algorithm. This result proved that the improved model showed higher efficiency in identifying green pepper.

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

Citation Download Citation

Fanbo Xiao and Dejin Zhao "Green pepper recognition algorithm based on improved YOLOv7", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 132720G (3 October 2024); https://doi.org/10.1117/12.3048065

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KEYWORDS

Detection and tracking algorithms

Object detection

Target detection

Feature fusion

Education and training

Feature extraction

Performance modeling

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