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SINGLE BANANA APPEARANCE GRADING WITH PPYOLO-BANANA

文献类型： 外文期刊

作者： Mao, Dianhui 1 ; Zhang, Denghui 1 ; Wang, Xuesen 1 ; Lv, Dongdong 1 ; Wu, Jianwei 3 ; Chen, Junhua 5 ;

作者机构： 1.Beijing Technol & Business Univ, Beijing Key Lab Big Data Technol Food Safety Comp, Beijing, Peoples R China

2.Beijing Technol & Business Univ, Natl Engn Lab Agri Prod Qual Traceabil, Beijing, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing, Peoples R China

4.Beijing PAIDE Sci & Technol Dev Co Ltd, Beijing, Peoples R China

5.China Natl Inst Standardizat, Standardizat Theory & Strategy, Beijing, Peoples R China

关键词： Banana defect recognition; Banana appearance grading; CustomPAN; DIoULoss; PPYOLOE plus

期刊名称：APPLIED ENGINEERING IN AGRICULTURE （ 影响因子：0.9； 五年影响因子：1.1 ）

ISSN： 0883-8542

年卷期： 2023 年 39 卷 5 期

页码：

收录情况： SCI

摘要： . With the development of the fruit individual packaging industry, the appearance quality of individually packaged fruits has put forward higher requirements. Due to the dense and uneven defects on the surface of bananas, the existing detection algorithms are prone to the problem of unrecognizable or degraded recognition accuracy. In this article, we propose an efficient banana surface defect detection model, the PPYOLO-Banana model. PPYOLO-Banana is based on the PPYOLOE+-m model with improved model structure and loss function, and the optimized CustomPAN can get more multilevel features, and compared with the original network PPYOLOE+-m model, the algorithm significantly improves the accuracy, with an average accuracy improvement of 2.2% (1.3% for the original image test set). mAP of PPYOLO-Banana is 97.0% (96.1% for the original image test set), which is 14.3% higher than the PPYOLOE model, and 10.9%, 8.9%, 8.9%, and 8.1% higher than the YOLOX, YOLOX-tiny, YOLOv5, and YOLOV4 models, respectively. The detection speed of the PPYOLO-Banana model is 17.71 frames per second, which is 2.95, 2.10, 1.90, and 0.98 times higher than that of YOLOv3, YOLOv4, YOLOX, and YOLOX-tiny, respectively. The results show that the proposed PPYOLO-Banana model achieves a balance between accuracy and speed in recognizing banana surface defects, improves the quality detection capability of individually packed fruits, it can effectively grade the quality of banana appearance, and has good potential to become an intelligent sorting machine.

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