Recognition and Detection of Persimmon in a Natural Environment Based on an Improved YOLOv5 Model

Accurate and rapid recognition of fruit is the guarantee of intelligent persimmon picking. Given the changes in the light and occlusion conditions in a natural environment, this study developed a detection method based on the improved YOLOv5 model. This approach has several critical steps, including...

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Bibliographic Details
Published inElectronics (Basel) Vol. 12; no. 4; p. 785
Main Authors Cao, Ziang, Mei, Fangfang, Zhang, Dashan, Liu, Bingyou, Wang, Yuwei, Hou, Wenhui
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.02.2023
Subjects
Accuracy
Algorithms
Datasets
Fruits
Image contrast
Image enhancement
Leak detection
Mathematical optimization
Methods
Model accuracy
Object recognition (Computers)
Occlusion
Pattern recognition
Persimmon
Persimmons
Recognition
Target detection
China
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Summary:Accurate and rapid recognition of fruit is the guarantee of intelligent persimmon picking. Given the changes in the light and occlusion conditions in a natural environment, this study developed a detection method based on the improved YOLOv5 model. This approach has several critical steps, including optimizing the loss function based on the traditional YOLOv5, combining the centralized feature pyramid (CFP), integrating the convolutional block attention module (CBAM), and adding a small target detection layer. Images of ripe and unripe persimmons were collected from fruit trees. These images were preprocessed to enhance the contrast, and they were then extended by means of image enhancement to increase the robustness of the network. To test the proposed method, several experiments, including detection and comparative experiments, were conducted. From the detection experiments, persimmons in a natural environment could be detected successfully using the proposed model, with the accuracy rate reaching 92.69%, the recall rate reaching 94.05%, and the average accuracy rate reaching 95.53%. Furthermore, from the comparison experiments, the proposed model performed better than the traditional YOLOv5 and single-shot multibox detector (SSD) models, improving the detection accuracy while reducing the leak detection and false detection rate. These findings provide some references for the automatic picking of persimmons.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12040785