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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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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