Vegetable Detection Algorithm Based on Improved YOLO v3
[1]Wei Hongbin,Zhang Duanjin,Du Guangming,et al.Vegetable Detection Algorithm Based on Improved YOLO v3[J].Journal of Zhengzhou University (Engineering Science),2020,41(02):7-12.[doi:10.13705/j.issn.1671-6833.2020.03.002]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
41卷
Number of periods:
2020 02
Page number:
7-12
Column:
Public date:
2020-05-31
- Title:
- Vegetable Detection Algorithm Based on Improved YOLO v3
- CLC:
- -
- DOI:
- 10.13705/j.issn.1671-6833.2020.03.002
- Abstract:
- The queuing and weighing problem was common in bulk vegetable area of supermarket. If weighingequipment could automatically recognize vegetable, it would effectively improve the operational efficiency ofsupermarket. Therefore, a vegetable recognition method based on improved YOLOv3 was proposed. Firstly ,vegetable pictures were collected by using high-definition camera and web crawler technology. Secondly, 15groups of anchors suitable for vegetable datasets were obtained by K -means clustering analysis. Thirdly, a newbounding box regression loss function DIoU was proposed to improve the precision of detection task. Finally, asthere were many large objects in vegetable datasets, 5 groups of feature pyramids with different scales were ob-tained by enhancing feature extraction network to realize vegetable detection task. The mAP of the improvedYOLOv3 algorithm on the test dataset was 93. 2%, and the recognition rate was 35 fps. This method improvedthe recognition of mAP while guaranteeing real-time object detection.
- References:
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Last Update:
2020-05-30