Green litchi automatic learning based on YOLOX-S, Faster-RCNN, SSD deep learning algorithm

Maoling Yang; Chenglin Wang

doi:10.1117/12.3012092

21 December 2023 Green litchi automatic learning based on YOLOX-S, Faster-RCNN, SSD deep learning algorithm

Maoling Yang, Chenglin Wang

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 1297039 (2023) https://doi.org/10.1117/12.3012092
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

In the past 10 years, many models of target detection based on convolution networks have been proposed, which are mainly divided into single-stage target detection models and two-stage target detection models. The main feature of single-stage is fast detection speed, and the main feature of two-stage is high detection accuracy. The purpose of this experiment is to select the most suitable target detection model for Qinglitchi by comparing the performance of the single-stage target detection model and the two-stage target detection model. After many and sufficient model training, the experimental results show that the YOLOX-S model has the fastest detection speed, and FPS is 37.965; The average accuracy of detection is the highest, with an average accuracy of 89.98%. Therefore, the YOLOX-S model is selected as the target detection model of green litchi.

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

Citation Download Citation

Maoling Yang and Chenglin Wang "Green litchi automatic learning based on YOLOX-S, Faster-RCNN, SSD deep learning algorithm", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 1297039 (21 December 2023); https://doi.org/10.1117/12.3012092

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