Abstract
In the process of container loading and unloading, manual inspection is still used in the process of container lead seal inspection, which has the problems of low efficiency, high labor cost, and high safety risk. Using visual object detection technology to replace manual lead seal automatic detection technology is an effective way to improve the efficiency of container operation. To address the problem of the tiny area of the seal in the image, the significant variation in scale, and the random location of its appearance, this paper proposes a Nano-CenterNet model. Based on the CenterNet, the lightweight feature extraction network is introduced, and the lightweight feature fusion network is added; the enhancement module was used to enhance the small object feature. The loss function of the algorithm is optimized to improve the imbalance between positive and negative samples. The Nano-CenterNet model was applied to the detection of container lead seals. The 3200 samples collected at the port entrance were used as the training set, and 400 samples were used as the test set. The measured precision rate was 96.5%, the recall rate was 95.4%, and the detection speed reached 18FPS, which met industrial application requirements.
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References
Ashraf, M.W., Sultani, W., Shah, M.: Dogfight: detecting drones from drones videos (2021)
Deshmukh, V.R., Patnaik, G.K., Patil, M.E.: Real-time traffic sign recognition system based on colour image segmentation. Int. J. Comput. Appl. 83(3), 30–35 (2013)
Kampffmeyer, M., Salberg, A.B., Jenssen, R.: Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–9 (2016)
Gonalves, B.C., Spitzbart, B., Lynch, H.J.: SealNet: a fully-automated pack-ice seal detection pipeline for sub-meter satellite imagery. Remote Sens. Environ. 239, 111617 (2020)
Zhou, X., Wang, D., Krhenbühl, P.: Objects as points (2019)
Ma, N., Zhang, X., Zheng, H.-T., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 122–138. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01264-9_8
Liu, S., Qi, L., Qin, H., et al.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759–8768 (2018)
Han, K., Wang, Y., Tian, Q., et al.: GhostNet: more features from cheap operations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1580–1589 (2020)
Najibi, M., Samangouei, P., Chellappa, R., et al.: SSH: Single-stage headless face detector. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4875–4884 (2017)
Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1
Li, B., Liu, Y., Wang, X.: Gradient harmonized single-stage detector. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 8577–8584 (2019)
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Zhang, G., Guo, J., Liu, Q., Wang, H. (2022). Container Lead Seal Detection Based on Nano-CenterNet. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1637. Springer, Singapore. https://doi.org/10.1007/978-981-19-6142-7_16
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DOI: https://doi.org/10.1007/978-981-19-6142-7_16
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