Detecting container box numbers based on improved YOLOv5 network

Shuo Xu; Chunlong Yao

doi:10.1117/12.3038519

23 August 2024 Detecting container box numbers based on improved YOLOv5 network

Shuo Xu, Chunlong Yao

Author Affiliations +

Proceedings Volume 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024); 132502E (2024) https://doi.org/10.1117/12.3038519
Event: Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024), 2024, Kuala Lumpur, Malaysia

Abstract

Efficient container transportation management relies heavily on accurate and timely detection of container box numbers. Recognizing the limitations of current manual recording and traditional detection methods, we present an enhanced YOLOv5-based algorithm. This algorithm incorporates a spatial channel attention mechanism to bolster target feature extraction and undergoes iterative training for refinement. Experimental results showcase a remarkable average detection accuracy of 98.5% and a swift detection frame rate of 54 FPS. In comparison to conventional methods, our approach significantly improves detection performance, enabling real-time capabilities suitable for diverse operational environments in container transportation management.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shuo Xu and Chunlong Yao "Detecting container box numbers based on improved YOLOv5 network", Proc. SPIE 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024), 132502E (23 August 2024); https://doi.org/10.1117/12.3038519

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