An occluded cherry tomato recognition model based on improved YOLOv7

Frontiers in Plant Science

Plant Science

Runxin Niu¹  Guangyu Hou²  Chen Hua²  Mingkun Jiang²  Chunmao Jiang²  Haihua Chen³  Yike Ma³ 

[1] Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China;Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China;Science Island Branch, University of Science and Technology of China Country, Hefei, China;Institute of Computer Science, Chinese Academy of Sciences, Beijing, China;

关键词: cherry tomato picking robot;    object detection;    depth separable convolution;    residual module;    coordinate attention mechanism;    DSP-YOLOv7-CA;   

DOI  :  10.3389/fpls.2023.1260808

 received in 2023-07-18, accepted in 2023-10-02,  发布年份 2023

来源: Frontiers

【 摘 要 】

The typical occlusion of cherry tomatoes in the natural environment is one of the most critical factors affecting the accurate picking of cherry tomato picking robots. To recognize occluded cherry tomatoes accurately and efficiently using deep convolutional neural networks, a new occluded cherry tomato recognition model DSP-YOLOv7-CA is proposed. Firstly, images of cherry tomatoes with different degrees of occlusion are acquired, four occlusion areas and four occlusion methods are defined, and a cherry tomato dataset (TOSL) is constructed. Then, based on YOLOv7, the convolution module of the original residual edges was replaced with null residual edges, depth-separable convolutional layers were added, and jump connections were added to reuse feature information. Then, a depth-separable convolutional layer is added to the SPPF module with fewer parameters to replace the original SPPCSPC module to solve the problem of loss of small target information by different pooled residual layers. Finally, a coordinate attention mechanism (CA) layer is introduced at the critical position of the enhanced feature extraction network to strengthen the attention to the occluded cherry tomato. The experimental results show that the DSP-YOLOv7-CA model outperforms other target detection models, with an average detection accuracy (mAP) of 98.86%, and the number of model parameters is reduced from 37.62MB to 33.71MB, which is better on the actual detection of cherry tomatoes with less than 95% occlusion. Relatively average results were obtained on detecting cherry tomatoes with a shade level higher than 95%, but such cherry tomatoes were not targeted for picking. The DSP-YOLOv7-CA model can accurately recognize the occluded cherry tomatoes in the natural environment, providing an effective solution for accurately picking cherry tomato picking robots.

【 授权许可】

Unknown   
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