A Size-Grading Method of Antler Mushrooms Using YOLOv5 and PSPNet
Quality grading in antler mushroom industrial production is a labor-intensive operation. For a long time, manual grading has been used for grading, which produces various problems such as insufficient reliability, low production efficiency, and high mushroom body damage. Automatic grading is a probl...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/12/11/2601 |
| _version_ | 1797469515800379392 |
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| author | Yanqiang Wu Yongbo Sun Shuoqin Zhang Xia Liu Kai Zhou Jialin Hou |
| author_facet | Yanqiang Wu Yongbo Sun Shuoqin Zhang Xia Liu Kai Zhou Jialin Hou |
| author_sort | Yanqiang Wu |
| collection | DOAJ |
| description | Quality grading in antler mushroom industrial production is a labor-intensive operation. For a long time, manual grading has been used for grading, which produces various problems such as insufficient reliability, low production efficiency, and high mushroom body damage. Automatic grading is a problem to be solved urgently for antler mushroom industrial development with increasing labor costs. To solve the problem, this paper deeply integrates the single-stage object detection of YOLOv5 and the semantic segmentation of PSPNet, and proposes a Y-PNet model for real-time object detection and an image segmentation network. This article also proposes an evaluation model for antler mushroom’s size, which eliminates subjective judgment and achieves quality grading. Moreover, to meet the needs of efficient and accurate hierarchical detection in the factory, this study uses the lightweight network model to construct a lightweight YOLOv5 single-stage object detection model. The MobileNetV3 network model embedded with a CBAM module is used as the backbone extractor in PSPNet to reduce the model’s size and improve the model’s efficiency and accuracy for segmentation. Experiments show that the proposed system can perform real-time grading successfully, which can provide instructive and practical references in industry. |
| first_indexed | 2024-03-09T19:22:30Z |
| format | Article |
| id | doaj.art-f2ee635d5590477f9fa2e9658be3465f |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-09T19:22:30Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-f2ee635d5590477f9fa2e9658be3465f2023-11-24T03:19:04ZengMDPI AGAgronomy2073-43952022-10-011211260110.3390/agronomy12112601A Size-Grading Method of Antler Mushrooms Using YOLOv5 and PSPNetYanqiang Wu0Yongbo Sun1Shuoqin Zhang2Xia Liu3Kai Zhou4Jialin Hou5College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, ChinaCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, ChinaPhysical Education College, Shandong Agricultural University, Taian 271018, ChinaShandong Nongfa Mushroom Industry Group Co., Ltd., Dongying 257029, ChinaCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, ChinaCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, ChinaQuality grading in antler mushroom industrial production is a labor-intensive operation. For a long time, manual grading has been used for grading, which produces various problems such as insufficient reliability, low production efficiency, and high mushroom body damage. Automatic grading is a problem to be solved urgently for antler mushroom industrial development with increasing labor costs. To solve the problem, this paper deeply integrates the single-stage object detection of YOLOv5 and the semantic segmentation of PSPNet, and proposes a Y-PNet model for real-time object detection and an image segmentation network. This article also proposes an evaluation model for antler mushroom’s size, which eliminates subjective judgment and achieves quality grading. Moreover, to meet the needs of efficient and accurate hierarchical detection in the factory, this study uses the lightweight network model to construct a lightweight YOLOv5 single-stage object detection model. The MobileNetV3 network model embedded with a CBAM module is used as the backbone extractor in PSPNet to reduce the model’s size and improve the model’s efficiency and accuracy for segmentation. Experiments show that the proposed system can perform real-time grading successfully, which can provide instructive and practical references in industry.https://www.mdpi.com/2073-4395/12/11/2601antler mushroomdeep learningreal timesize grading |
| spellingShingle | Yanqiang Wu Yongbo Sun Shuoqin Zhang Xia Liu Kai Zhou Jialin Hou A Size-Grading Method of Antler Mushrooms Using YOLOv5 and PSPNet Agronomy antler mushroom deep learning real time size grading |
| title | A Size-Grading Method of Antler Mushrooms Using YOLOv5 and PSPNet |
| title_full | A Size-Grading Method of Antler Mushrooms Using YOLOv5 and PSPNet |
| title_fullStr | A Size-Grading Method of Antler Mushrooms Using YOLOv5 and PSPNet |
| title_full_unstemmed | A Size-Grading Method of Antler Mushrooms Using YOLOv5 and PSPNet |
| title_short | A Size-Grading Method of Antler Mushrooms Using YOLOv5 and PSPNet |
| title_sort | size grading method of antler mushrooms using yolov5 and pspnet |
| topic | antler mushroom deep learning real time size grading |
| url | https://www.mdpi.com/2073-4395/12/11/2601 |
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