# A Two-Stage Automatic Container Code Recognition Method Considering Environmental Interference
## 5 Conclusions  
This study introduces a dual-stage container number recognition method leveraging deep learning techniques, specifically designed to swiftly and accurately identify container numbers at terminal gates. The proposed approach incorporates the enhanced algorithms C-YOLOv4 for detecting container number areas and C-Deeplabv3+ for character recognition. This method not only maintains high accuracy but also significantly accelerates recognition speed and reduces the complexity of the model, making it more practical for deployment at terminal facilities. Significant enhancements were applied to YOLOv4, including replacing the CSPDarknet53 network with MobileNetv3 and substituting PANet with FPN to reduce parameters. The removal of 13 \(\times\) 13 scale feature maps and the recalibration of prior box values have tailored the model to better fit the container number localization task. Furthermore, the adoption of an Enhanced Intersection Over Union (EIOU) loss function has optimized the speed of model training. The modified C-YOLOv4 demonstrated a 30% improvement in speed and a 1.06% increase in accuracy over the original model, outperforming other conventional models like YOLOv3 and YOLOv5s in complex scenarios. Modifications to Deeplabv3+ were also pivotal. Replacing Xception with MobileNetv3 as the backbone feature-extraction network reduced parameter size. The introduction of a new atrous convolution branch and a redesigned Atrous Spatial Pyramid Pooling (ASPP) module have improved the model's precision in detecting small targets. Adjustments in the decoder, specifically the shift to transposed and sub-pixel convolution upsampling, have effectively preserved image details, boosting the character recognition capabilities of C-Deeplabv3+. Compared to its predecessor, the updated model has shown a 23.9% faster recognition speed and a 1.58% higher accuracy rate. However, the slower operation of C-Deeplabv3+ compared to C-YOLOv4 suggests the need for further optimization. In future work, we will explore how to adapt our algorithms and models to various IoT device platforms and optimize their performance to ensure efficient operation under diverse hardware and resource constraints. Additionally, we will focus on diversifying the dataset to enhance the algorithm's generalization ability. Through these efforts, we aim to extend the reach of our container number recognition system to a broader range of IoT devices and have a greater impact on actual port operations. Future efforts will focus on these areas to advance the system's application in real-world port operations. This research provides a solid foundation for robust, efficient container number recognition systems, with potential for further enhancement and application in real-world port operations. Figure 14: General demonstration of the recognition effect of the algorithm. **Author Contributions:** Conceptualization, M.Y. and S.Z. ; methodology, M.Y. and S.Z. ; validation, M.Y., S.Z., B.L. and T.W. ; formal analysis, B.L. ; resources, Q.C. ; data curation, S.Z. ; writing--original draft preparation, S.Z. and B.L. ; writing--review and editing, M.Y. and Q.C. ; visualization, S.Z. ; supervision, T.W. ; project administration, M.Y. ; funding acquisition, M.Y. and T.W. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the National Key R&D Program of China (2020 YFB1712400) and the National Natural Science Foundation of China (No. 52272423, No. 71672137). **Instructional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. **Conflicts of Interest:** Author [PERSON] was employed by the company China Ocean Shipping Agency Tianjin Co., Ltd. Author [PERSON] was employed by the company Qingdao New Qianwan Container Terminal Co., Ltd.