EDEM-YOLO: an improved lightweight multi-scale feature fusion model for tomato variety maturity detection

Jiahui Zhu; Xiaoyun Xie

doi:10.1117/12.3041034

11 September 2024 EDEM-YOLO: an improved lightweight multi-scale feature fusion model for tomato variety maturity detection

Jiahui Zhu, Xiaoyun Xie

Author Affiliations +

Proceedings Volume 13253, Fourth International Conference on Signal Image Processing and Communication (ICSIPC 2024); 132531E (2024) https://doi.org/10.1117/12.3041034
Event: 4th International Conference on Signal Image Processing and Communication, 2024, Xi'an, China

Abstract

YOLOv7 algorithm is widely used in fruit target detection, but there are issues including intricate construction, expensive computing, and low precision. The aforementioned issues can be addressed through the implementation of an enhanced algorithm. EDEM-YOLO based on YOLOv7 was proposed to detect the maturity of two common tomato varieties, big tomato and cherry tomato. The original backbone network of YOLOv7 is replaced with a lightweight feature extraction network model, Efficient Vision Transformer, in order to enhance the model's simplicity. In addition, in order to optimize the deviation of the width and height of the bounding box, the loss function of the improved model was replaced by MPDiou, in order to enhance the information extraction ability of the model for small objects. EMA (Efficient Multi-Scale Attention) and Dynamic head object detection head based on attention mechanism are fused in the head network layer. The experimental results show that the EDEM-YOLO algorithm model achieves high accuracy and detection efficiency. Without using the original pre-training weights of YOLOv7, the Mean Average Precision (mAP) of maturity detection is 81.7%, which is 1.5%higher than that of the original YOLOv7 and the calculation amount is reduced by 62.3%. The number of parameters is reduced by 24.1%. The EDEM-YOLOv7 model shows excellent detection ability in similar object detection models, and can be applied to various complex mechanical automatic detection tasks.

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

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Jiahui Zhu and Xiaoyun Xie "EDEM-YOLO: an improved lightweight multi-scale feature fusion model for tomato variety maturity detection", Proc. SPIE 13253, Fourth International Conference on Signal Image Processing and Communication (ICSIPC 2024), 132531E (11 September 2024); https://doi.org/10.1117/12.3041034

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KEYWORDS

Object detection

Detection and tracking algorithms

Head

Education and training

Feature extraction

Atmospheric modeling

Convolution

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