Towards infield, live plant phenotyping using a reduced-parameter CNN
Tóm tắt
There is an increase in consumption of agricultural produce as a result of the rapidly growing human population, particularly in developing nations. This has triggered high-quality plant phenotyping research to help with the breeding of high-yielding plants that can adapt to our continuously changing climate. Novel, low-cost, fully automated plant phenotyping systems, capable of infield deployment, are required to help identify quantitative plant phenotypes. The identification of quantitative plant phenotypes is a key challenge which relies heavily on the precise segmentation of plant images. Recently, the plant phenotyping community has started to use very deep convolutional neural networks (CNNs) to help tackle this fundamental problem. However, these very deep CNNs rely on some millions of model parameters and generate very large weight matrices, thus making them difficult to deploy infield on low-cost, resource-limited devices. We explore how to compress existing very deep CNNs for plant image segmentation, thus making them easily deployable infield and on mobile devices. In particular, we focus on applying these models to the pixel-wise segmentation of plants into multiple classes including background, a challenging problem in the plant phenotyping community. We combined two approaches (separable convolutions and SVD) to reduce model parameter numbers and weight matrices of these very deep CNN-based models. Using our combined method (separable convolution and SVD) reduced the weight matrix by up to 95% without affecting pixel-wise accuracy. These methods have been evaluated on two public plant datasets and one non-plant dataset to illustrate generality. We have successfully tested our models on a mobile device.
Tài liệu tham khảo
Aich, S., Stavness, I.: Leaf counting with deep convolutional and deconvolutional networks. (2017) arXiv preprint arXiv:1708.07570
Aich, S., Stavness, I.: Object counting with small datasets of large images. (2018) arXiv preprint arXiv:1805.11123
Aich, S., Josuttes, A., Ovsyannikov, I., Strueby, K., Ahmed, I., Duddu, H.S., Pozniak, C., Shirtliffe, S., Stavness, I.: Deepwheat: Estimating phenotypic traits from crop images with deep learning. In: IEEE Winter Conference on Applications of Computer Vision (WACV), 2018, IEEE, pp 323–332 (2018)
Aich, S., van der Kamp, W., Stavness, I.: Semantic binary segmentation using convolutional networks without decoders. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE, pp. 182–1824 (2018)
Alexandratos, N., Bruinsma, J. et al.: World agriculture towards 2030/2050: the 2012 revision. Tech. rep., ESA Working paper FAO, Rome (2012)
Atanbori, J., Chen, F., French, A.P., Pridmore, T.: Towards low-cost image-based plant phenotyping using reduced-parameter cnn. In: S A Tsaftaris HS, Pridmore T (eds) Proceedings of the Computer Vision Problems in Plant Phenotyping (CVPPP), BMVA Press, (2018) http://bmvc2018.org/contents/workshops/cvppp2018/0023.pdf
Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)
Chollet, F.: Xception: Deep learning with depthwise separable convolutions. (2016) arXiv preprint
Denton, E.L., Zaremba, W., Bruna, J., LeCun, Y., Fergus, R.: Exploiting linear structure within convolutional networks for efficient evaluation. In: Advances in neural information processing systems, pp 1269–1277 (2014)
Girshick, R.: Fast r-cnn. (2015) arXiv preprint arXiv:1504.08083
Giuffrida, M.V., Minervini, M., Tsaftaris, S.A.: Learning to count leaves in rosette plants (2016)
Han, S., Mao, H., Dally, W.J.: Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. (2015) arXiv preprint arXiv:1510.00149
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. (2017) arXiv preprint arXiv:1704.04861
Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and \(< 0.5~\text{mb}\) model size. (2016) arXiv preprint arXiv:1602.07360
Jin, J., Dundar, A., Culurciello, E.: Flattened convolutional neural networks for feedforward acceleration. (2014) arXiv preprint arXiv:1412.5474
Jin, X., Liu, S., Baret, F., Hemerlé, M., Comar, A.: Estimates of plant density of wheat crops at emergence from very low altitude uav imagery. Remote Sens. Environ. 198, 105–114 (2017)
Lin, G., Milan, A., Shen, C., Reid, I.: Refinenet: Multi-path refinement networks for high-resolution semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
Liu, S., Baret, F., Andrieu, B., Burger, P., Hemmerle, M.: Estimation of wheat plant density at early stages using high resolution imagery. Front. Plant Sci. 8, 739 (2017)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440 (2015)
Minervini, M., Fischbach, A., Scharr, H., Tsaftaris, S.: Plant phenotyping datasets. (2015) http://www.plant-phenotyping.org/datasets
Minervini, M., Fischbach, A., Scharr, H., Tsaftaris, S.A.: Finely-grained annotated datasets for image-based plant phenotyping. Pattern Recogn. Lett. 81, 80–89 (2016)
Minervini, M., Giuffrida, M.V., Tsaftaris, S.A.: An interactive tool for semi-automated leaf annotation (2016)
Nilsback, M.E., Zisserman, A.: Delving deeper into the whorl of flower segmentation. Image Vis. Comput. 28(6), 1049–1062 (2010)
Pound, M.P., Atkinson, J.A., Townsend, A.J., Wilson, M.H., Griffiths, M., Jackson, A.S., Bulat, A., Tzimiropoulos, G., Wells, D.M., Murchie, E.H., et al.: Deep machine learning provides state-of-the-art performance in image-based plant phenotyping. GigaScience (2017)
Pound, M.P., Atkinson, J.A., Wells, D.M., Pridmore, T.P., French, A.P.: Deep learning for multi-task plant phenotyping. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2055–2063 (2017)
Razafindradina, H.B., Randriamitantsoa, P.A., Razafindrakoto, N.R.: Image compression with svd: A new quality metric based on energy ratio. (2017) arXiv preprint arXiv:1701.06183
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
Scharr, H., Minervini, M., French, A.P., Klukas, C., Kramer, D.M., Liu, X., Luengo, I., Pape, J.M., Polder, G., Vukadinovic, D., et al.: Leaf segmentation in plant phenotyping: a collation study. Mach. Vis. Appl. 27(4), 585–606 (2016)
Shi, W., Caballero, J., Huszár, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1874–1883 (2016)
Shrestha, D.S., Steward, B.L.: Automatic corn plant population measurement using machine vision. Trans. ASAE 46(2), 559 (2003)
Sun, Y., Zheng, L., Deng, W., Wang, S.: Svdnet for pedestrian retrieval. (2017) arXiv preprint
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., et al.: Going deeper with convolutions. Cvpr (2015)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Walter, A., Liebisch, F., Hund, A.: Plant phenotyping: from bean weighing to image analysis. Plant Methods 11(1), 14 (2015)
Wang, M., Liu, B., Foroosh, H.: Factorized convolutional neural networks. (2016) CoRR, arXiv:1608.04337
Wu, J., Leng, C., Wang, Y., Hu, Q., Cheng, J.: Quantized convolutional neural networks for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4820–4828 (2016)
Xue, J., Li, J., Gong, Y.: Restructuring of deep neural network acoustic models with singular value decomposition. In: Interspeech, pp. 2365–2369 (2013)
Yu, D., Seide, F., Li, G., Deng, L.: Exploiting sparseness in deep neural networks for large vocabulary speech recognition. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012, IEEE, pp. 4409–4412 (2012)
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. (2015) arXiv preprint arXiv:1511.07122
Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: Computer Vision and Pattern Recognition, vol 1 (2017)
Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: An extremely efficient convolutional neural network for mobile devices. (2017) arXiv preprint arXiv:1707.01083
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 2881–2890 (2017)