Abstract
The identification of container surfaces carries a large amount of crucial information regarding production and logistics. Research on the detection and identification of containers is lacking in both academia and industry, and the efficiency is low due to the need for manual completion of related tasks. In order to tackle this problem, we have created a large-scale text detection dataset for container surface identification called IdentificationText. This dataset consists of 12,000 high-resolution images, providing bounding boxes annotations for text detection tasks. We have discussed downstream applications of the IdentificationText dataset as well as our annotation techniques used in the dataset’s creation. The text in this dataset exhibits challenges such as deformations, multi-direction, and multi-scale. We conducted extensive experiments to evaluate the effectiveness and difficulty of this dataset using advanced text detection methods. In our experiments, we found that repeated textures and vertical text at multiple scales would cause missed detections, which was an extremely serious problem. The experimental results indicate that it is challenging for current text detection methods to locating text on container surfaces. Achieving higher accuracy in detecting text on containers requires more in-depth research. The experimental results serve as the benchmark performance for the IdentificationText dataset, providing reference for future researchers.
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Acknowledgements
This work was supported by Guangdong Basic and Applied Basic Research Foundation (No. 2021A1515011576), Guangdong, Hong Kong, Macao and the Greater Bay Area International Science and Technology Innovation Cooperation Project (No. 2021A050530080, No. 2021A0505060011), Key Research Projects for the Universities of Guangdong Provincial Education Department (No. 2020ZDZX3031, No. 2022ZDZX1032), Jiangmen Basic and Applied Basic Research Key Project (2021030103230006670), Jiangmen Science and Technology Plan Project (2220002000246), and Key Laboratory of Public Big Data in Guizhou Province (No. 2019BDKFJJ015), Development of a Container Intelligent Panoramic Trademark Quality Inspection System Based on Machine Vision(HX22105).
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Liang, Z. et al. (2024). Performance Evaluation of Container Identification Detection Algorithm. In: Kountchev, R., Patnaik, S., Nakamatsu, K., Kountcheva, R. (eds) Proceedings of International Conference on Artificial Intelligence and Communication Technologies (ICAICT 2023). ICAICT 2023. Smart Innovation, Systems and Technologies, vol 368. Springer, Singapore. https://doi.org/10.1007/978-981-99-6641-7_21
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DOI: https://doi.org/10.1007/978-981-99-6641-7_21
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