Detection of cherry defects based on improved Faster R-CNN model

Authors

WEI Ran, Beidou High Precision Positioning Service Technology Engineering Laboratory of Liaoning Province, Dalian, Liaoning 116622, China; Dalian University Environment Sensing and Intelligent Control Key Laboratory of Dalian, Dalian University, Dalian, Liaoning 116622, China
PEI Yue-kun, Beidou High Precision Positioning Service Technology Engineering Laboratory of Liaoning Province, Dalian, Liaoning 116622, China; Dalian University Environment Sensing and Intelligent Control Key Laboratory of Dalian, Dalian University, Dalian, Liaoning 116622, China
JIANG Yan-chao, Beidou High Precision Positioning Service Technology Engineering Laboratory of Liaoning Province, Dalian, Liaoning 116622, China; Dalian University Environment Sensing and Intelligent Control Key Laboratory of Dalian, Dalian University, Dalian, Liaoning 116622, China
ZHOU Pin-zhi, Beidou High Precision Positioning Service Technology Engineering Laboratory of Liaoning Province, Dalian, Liaoning 116622, China; Dalian University Environment Sensing and Intelligent Control Key Laboratory of Dalian, Dalian University, Dalian, Liaoning 116622, China
ZHANG Yong-fei, Beidou High Precision Positioning Service Technology Engineering Laboratory of Liaoning Province, Dalian, Liaoning 116622, China; Dalian University Environment Sensing and Intelligent Control Key Laboratory of Dalian, Dalian University, Dalian, Liaoning 116622, China

Abstract

Objective: To improve the efficiency of cherry classification and sorting in industrial environment. Methods: An improved cherry defect recognition and sorting model based on Faster R-CNN framework was proposed. Results: By comparing VGG16, MobileNet-V2 and ResNet50 network, the effect of Resnet50 network was the best, the improved Faster R-CNN model had 97.75%, 99.77%, 98.90%, 97.56%, 96.67%, 98.80% of detection precision for cherry fissure, twinning, growth stimulation, mildew, Browning rotten and intact fruit, respectively. The average detection accuracy of the improved Faster R-CNN model was 98.24%, which was higher than other models, and the detection speed was 31.16 frames/s. Conclusion: The test method had a high identification accuracy for cherry defects.

Publication Date

10-28-2021

First Page

98

Last Page

105,201

DOI

10.13652/j.issn.1003-5788.2021.10.018

Recommended Citation

Ran, WEI; Yue-kun, PEI; Yan-chao, JIANG; Pin-zhi, ZHOU; and Yong-fei, ZHANG (2021) "Detection of cherry defects based on improved Faster R-CNN model," Food and Machinery: Vol. 37: Iss. 10, Article 18.
DOI: 10.13652/j.issn.1003-5788.2021.10.018
Available at: https://www.ifoodmm.cn/journal/vol37/iss10/18

References

[1] 曹玉栋, 祁伟彦, 李娴, 等. 苹果无损检测和品质分级技术研究进展及展望[J]. 智慧农业, 2019（3）: 29-45.
[2] 徐赛, 陆华忠, 丘广俊, 等. 水果品质无损检测研究进展及应用现状[J]. 广东农业科学, 2020, 47（12）: 229-236.
[3] SIEDLISKA A, ZUBIK M, BARANOWSKI P, et al. Algorithms for detecting cherry pits on the basis of transmittance mode hyperspectral data[J]. International Agrophysics, 2017, 31（4）: 539.
[4] ZAWBAA H M, HAZMAN M, ABBASS M, et al. Automatic fruit classification using random forest algorithm[C]// 2014 14th International Conference on Hybrid Intelligent Systems. Kuwait: Institute of Electrical and Electronics Engineering, 2014: 164-168.
[5] SUBASI A, DAMMAS D H, ALGHAMDI R D, et al. Sensor based human activity recognition using adaboost ensemble classifier[J]. Procedia Computer Science, 2018, 140: 104-111.
[6] FARIS H, HASSONAH M A, ALA'M A Z, et al. A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture[J]. Neural Computing and Applications, 2018, 30（8）: 2 355-2 369.
[7] FELZENSZWALB P F, GIRSHICK R B, MCALLESTER D, et al. Object detection with discriminatively trained part-based models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 32（9）: 1 627-1 645.
[8] 裴悦琨, 连明月, 姜艳超, 等. 基于机器视觉的樱桃缺陷检测与识别[J]. 食品与机械, 2019, 35（12）: 137-140, 226.
[9] WANG Lin, ZHANG He-he. Application of faster R-CNN model in vehicle detection[J]. Journal of Computer Applications, 2018, 38（3）: 666-670.
[10] WAN Shao-hua, GOUDOS S. Faster R-CNN for multi-class fruit detection using a robotic vision system[J]. Computer Networks, 2020, 168: 107036.
[11] FU Long-sheng, MAJEED Y, ZHANG Xin, et al. Faster R-CNN-based apple detection in dense-foliage fruiting-wall trees using RGB and depth features for robotic harvesting[J]. Biosystems Engineering, 2020, 197: 245-256.
[12] DAI Xiao-biao, HU Jun-ping, ZHANG Hong-mei, et al. Multi-task Faster R-CNN for nighttime pedestrian detection and distance estimation[J]. Infrared Physics & Technology, 2021, 115: 103694.
[13] BARGOTI S, UNDERWOOD J. Deep fruit detection in orchards[C]// 2017 IEEE International Conference on Robotics and Automation （ICRA）. Singapore: Institute of Electrical and Electronics Engineering, 2017: 3 626-3 633.
[14] VILLACRS J F, AUAT CHEEIN F. Detection and characterization of cherries: A deep learning usability case study in chile[J]. Agronomy, 2020, 10（6）: 835.
[15] VASCONEZ J P, DELPIANO J, VOUGIOUKAS S, et al. Comparison of convolutional neural networks in fruit detection and counting: A comprehensive evaluation[J]. Computers and Electronics in Agriculture, 2020, 173: 105348.
[16] HE Kai-ming, ZHANG Xiang-yu, REN Shao-qing, et al. Deep residual learning for image recognition[C]// Proceedings of the IEEE conference on computer vision and pattern recognition. Las Vegas, NV, USA: Institute of Electrical and Electronics Engineering, 2016: 770-778.
[17] TSUNG-YI L, DOLLR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]// Proceedings of the IEEE conference on computer vision and pattern recognition. Honolulu, HI, USA: Institute of Electrical and Electronics Engineering, 2017: 2 117-2 125.
[18] REN Shao-qing,HE Kai-ming, GIRSHICK R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 39（6）: 1 137-1 149.
[19] 张顺, 龚怡宏, 王进军. 深度卷积神经网络的发展及其在计算机视觉领域的应用[J]. 计算机学报, 2019, 42（3）: 453-482.
[20] 梁鸿, 王庆玮, 张千, 等. 小目标检测技术研究综述[J]. 计算机工程与应用, 2021, 57（1）: 17-28.
[21] HU Jie, LI Shen, SUN Gang. Squeeze-and-excitation networks[C]// Proceedings of the IEEE conference on computer vision and pattern recognition. Salt Lake City, UT, USA: Institute of Electrical and Electronics Engineering, 2018: 7 132-7 141.
[22] LI Xiang, WANG Wen-hai, HU Xiao-lin, et al. Selective kernel networks[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, CA, USA: Institute of Electrical and Electronics Engineering, 2019: 510-519.