Abstract
Digital images and computer sciences have become two powerful tools in several areas, such as astronomy, medicine, forensics, etc. In the last years, computer sciences are getting involved in agricultural and food science to decide based on estimated or actual parameters named features. Rottenness is the state of decomposing or decaying the quality of the fruit, which not only affects the taste and appearance but also modifies its nutritional composition, causing the presence of mycotoxins dangerous for humans. Nowadays, rottenness detection is carried out using human inspection or using Ultraviolet light to highlight spots of rottenness represented as fluorescence. Recent computer vision approaches address this problem using hyperspectral imaging systems. In this paper, we propose to use a one-stage object detector inspired by RetinaNet to detect whether a fruit is fresh or rotten. One of the main stages of RetinaNet is based on computing a multi-scale convolutional feature pyramid network on top of a backbone. Therefore, in this work, we analyze the performance of RetinaNet using different artificial neural networks as backbone to determine the highest accuracy for fruit and rottenness detection. The experiments were done using a dataset composed of 13599 images divided by 6 classes, 3 fresh fruits, and 3 rotten fruits. The performance evaluation considers the mean average precision in the detection and the inference time of tested backbone models.
Supported by organization Universidad Panamericana.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Arzate-Vázquez, I., et al.: Image processing applied to classification of avocado variety hass (persea americana mill) during the ripening process. Food Bioprocess Technol. 4(7), 1307–1313 (2011)
Bhargava, A., Bansal, A.: Fruits and vegetables quality evaluation using computer vision: a review. Journal of King Saud University - Computer and Information Sciences, pp. 1–15 (2018)
Calvo, H., Moreno-Armendáriz, M.A., Godoy-Calderón, S.: A practical framework for automatic food products classification using computer vision and inductive characterization. Neurocomputing 175, 911–923 (2016)
Cárdenas-Pérez, S., et al.: Evaluation of the ripening stages of apple (golden delicious) by means of computer vision system. Biosyst. Eng. 159, 46–58 (2017)
da Costa, A.Z., Figueroa, H.E.H., Fracarolli, J.A.: Computer vision based detection of external defects on tomatoes using deep learning. Biosyst. Eng. 190, 131–144 (2020)
Fan, S., et al.: On line detection of defective apples using computer vision system combined with deep learning methods. J. Food Eng. 286, 110102 (2020)
Goel, L., Raman, S., Dora, S.S., Bhutani, A., Aditya, A.S., Mehta, A.: Hybrid computational intelligence algorithms and their applications to detect food quality. Artif. Intell. Rev. 53(2), 1415–1440 (2019). https://doi.org/10.1007/s10462-019-09705-8
Gómez-Sanchis, J., Martín-Guerrero, J.D., Soria-Olivas, E., Martínez-Sober, M., Magdalena-Benedito, R., Blasco, J.: Detecting rottenness caused by penicillium genus fungi in citrus fruits using machine learning techniques. Expert Syst. Appl. 39(1), 780–785 (2012)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980–2988 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Hoang, T.M., Nguyen, P.H., Truong, N.Q., Lee, Y.W., Park, K.R.: Deep retinanet-based detection and classification of road markings by visible light camera sensors. Sensors (Basel, Switz.) 19, 281 (2019)
ITU: H.264 : Advanced video coding for generic audiovisual services (2018). urlhttps://www.itu.int/rec/T-REC-H.264-201906-I/en
Jiao, L., et al.: A survey of deep learning-based object detection. IEEE Access 7, 128837–128868 (2019)
Kalluri, S.R.: Fruits: fresh and rotten for classification Dataset (2018). urlhttps://www.kaggle.com/sriramr/fruits-fresh-and-rotten-for-classification
Lin, T., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 318–327 (2020)
Lin, T.-Y., et al.: Microsoft COCO: Common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Liu, W., et al.: SSD: Single shot multibox detector. In: ECCV (2016)
Nosseir, A., Ahmed, S.E.A.: Automatic classification for fruits’ types and identification of rotten ones using k-nn and svm. Int. J. Online Biomed. Eng. 15(03), 47–61 (2019)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y
Zhang, Y., Wu, L.: Classification of fruits using computer vision and a multiclass support vector machine. Sensors (Basel, Switz.) 12, 12489–12505 (2012)
Zhu, X., Li, G.: Rapid detection and visualization of slight bruise on apples using hyperspectral imaging. Int. J. Food Prop. 22(1), 1709–1719 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Perez-Daniel, K., Fierro-Radilla, A., Peñaloza-Cobos, J.P. (2020). Rotten Fruit Detection Using a One Stage Object Detector. In: Martínez-Villaseñor, L., Herrera-Alcántara, O., Ponce, H., Castro-Espinoza, F.A. (eds) Advances in Computational Intelligence. MICAI 2020. Lecture Notes in Computer Science(), vol 12469. Springer, Cham. https://doi.org/10.1007/978-3-030-60887-3_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-60887-3_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60886-6
Online ISBN: 978-3-030-60887-3
eBook Packages: Computer ScienceComputer Science (R0)