Eggplant recognition algorithm based on improved YOLOv7-obb

Qi Liu; Dejin Zhao

doi:10.1117/12.3003798

11 October 2023 Eggplant recognition algorithm based on improved YOLOv7-obb

Qi Liu, Dejin Zhao

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128003N (2023) https://doi.org/10.1117/12.3003798
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

An enhanced version of the YOLOv7-obb deep learning neural network was proposed to achieve fast and accurate recognition of eggplant fruits and stems. The improvements primarily revolve around two key aspects. Firstly, the inclusion of the CBAM attention mechanism enhances the network's ability to perceive and extract target feature information by expanding the perception field of the feature layer. Secondly, the integration of rich image information from the first and second layers of the network backbone enables effective fusion of multi-scale features. Additionally, a dedicated output layer in the network's neck is introduced to enhance the detection of small targets and improve the extraction of relevant feature information. To evaluate the model's performance, extensive training experiments were conducted using a carefully curated eggplant dataset. The results demonstrate a significant improvement in the mean Average Precision (mAP) of the new model, with an increase from 76.74% to 88.84%. This indicates that the enhanced model exhibits strong recognition capabilities, showcasing its potential for intelligent eggplant harvesting.

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

Citation Download Citation

Qi Liu and Dejin Zhao "Eggplant recognition algorithm based on improved YOLOv7-obb", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128003N (11 October 2023); https://doi.org/10.1117/12.3003798

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available