Abstract
Particle detection aims to locate and count valid particles in pad images accurately. However, existing methods fail to achieve both high detection accuracy and inference efficiency in real applications. In order to keep a good trade-off between inference efficiency and accuracy, we propose a computation-efficient particle detection network (PAD-Net) with an encoder-decoder architecture. For the encoder part, MobileNetV3 is tailored to greatly reduce parameters at a little cost of accuracy drop. And the decoder part is designed based on the light-weight RefineNet, which further boosts particle detection performance. Besides, the proposed network is equipped with the adaptive attention loss (termed AAL), which improves the detection accuracy with a negligible increase in computation cost. Finally, we employ a knowledge distillation strategy to further boost the final detection performance of PAD-Net without increasing its parameters and floating-point operations (FLOPs). Experimental results on the real datasets demonstrate that our methods can achieve high-accuracy and real-time detection performance on valid particles compared with the state-of-the-art methods.
This work is supported by the National Natural Science Foundation of China (No. U1604262 and U1904211).
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References
He, T., Shen, C., Tian, Z., Gong, D., Sun, C., Yan, Y.: Knowledge adaptation for efficient semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, 16–20 June 2019, pp. 578–587 (2019)
Hinton, G.E., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. CoRR arXiv:abs/1503.02531 (2015)
Howard, A., et al.: Searching for mobilenetv3. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (2020)
Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. CoRR arXiv:abs/1704.04861 (2017)
Lin, C.S., Lu, K.H.H., Lin, T.C., Shei, H.J., Tien, C.L.: An automatic inspection method for the fracture conditions of anisotropic conductive film in the TFT-LCD assembly process. Int. J. Optomechatronics 5(9), 286–298 (2011)
Lin, G., Milan, A., Shen, C., Reid, I.D.: RefineNet: multi-path refinement networks for high-resolution semantic segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, 21–26 July 2017, pp. 5168–5177 (2017)
Lin, P., Sun, P., Cheng, G., Xie, S., Li, X., Shi, J.: Graph-guided architecture search for real-time semantic segmentation. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, 13–19 June 2020, pp. 4202–4211 (2020)
Liu, E., Chen, K., Xiang, Z., Zhang, J.: Conductive particle detection via deep learning for ACF bonding in TFT-LCD manufacturing. J. Intell. Manuf. 31(4), 1037–1049 (2020)
Liu, L., Chen, J., Wu, H., Chen, T., Li, G., Lin, L.: Efficient crowd counting via structured knowledge transfer. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 2645–2654 (2020)
Liu, Y., Chen, K., Liu, C., Qin, Z., Luo, Z., Wang, J.: Structured knowledge distillation for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, 16–20 June 2019, pp. 2604–2613 (2019)
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)
Nekrasov, V., Chen, H., Shen, C., Reid, I.D.: Fast neural architecture search of compact semantic segmentation models via auxiliary cells. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, 16–20 June 2019, pp. 9126–9135 (2019)
Nekrasov, V., Shen, C., Reid, I.: Light-weight refineNet for real-time semantic segmentation. In: British Machine Vision Conference 2018, BMVC 2018, Newcastle, UK, 3–6 September 2018, p. 125 (2018)
Ni, G., Liu, L., Du, X., Zhang, J., Liu, J., Liu, Y.: Accurate AOI inspection of resistance in LCD anisotropic conductive film bonding using differential interference contrast. Optik 130, 786–796 (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV 2: inverted residuals and linear bottlenecks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
Tian, Y., Lei, Y., Zhang, J., Wang, J.Z.: PaDNet: pan-density crowd counting. IEEE Trans. Image Process. 29, 2714–2727 (2020)
Wang, Y., et al.: LedNet: a lightweight encoder-decoder network for real-time semantic segmentation. In: 2019 IEEE International Conference on Image Processing, ICIP 2019, Taipei, Taiwan, 22–25 September 2019, pp. 1860–1864 (2019)
Wang, Y., Ma, L., Jiang, H.: Detecting conductive particles in TFT-LCD with U-multinet. In: 2019 8th International Symposium on Next Generation Electronics (ISNE), pp. 1–3. IEEE (2019)
Wang, Y., Ma, L., Jiu, M., Jiang, H.: Detection of conductive particles in TFT-LCD circuit using generative adversarial networks. IEEE Access PP(99), 1 (2020)
Yu-ye, C., Ke, X., Zhen-xiong, G., Jun-jie, H., Chang, L., Song-yan, C.: Detection of conducting particles bonding in the circuit of liquid crystal display. Chinese J. Liq. Cryst. Disp. 32(7), 553–559 (2017)
Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 18–22 June 2018, pp. 6848–6856 (2018)
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Wang, Y., Ma, L., Jian, L., Jiang, H. (2021). Efficient and Real-Time Particle Detection via Encoder-Decoder Network. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13022. Springer, Cham. https://doi.org/10.1007/978-3-030-88013-2_18
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