Research on the Algorithm of Position Correction for High-Speed Moving Express Packages Based on Traditional Vision...

Area of Science:

• Computer Vision
• Artificial Intelligence
• Logistics Technology

Background:

• The logistics industry faces challenges in efficiently sorting rapidly moving express parcels.
• Accurate and fast identification of parcels is crucial for optimizing logistics sorting system performance.

Purpose of the Study:

• To develop a novel motion express parcel positioning algorithm.
• To enhance parcel recognition accuracy and inference speed in logistics sorting.

Main Methods:

• A hybrid approach combining traditional brightness-based visual detection with AI-based vision.
• Enhancement of YOLOv5 using Convolutional Block Attention Module (CBAM) and Focal-EIoU.
• Implementation of Optimal Transport Assignment (OTA) for optimal training dataset generation.

Main Results:

• The AI-enhanced model demonstrated superior parcel recognition accuracy compared to traditional algorithms.
• The proposed algorithm achieved faster inference speeds, improving sorting efficiency.
• Experimental results validated the algorithm's applicability in practical logistics sorting scenarios.

Conclusions:

• The integrated traditional and AI vision approach offers a robust solution for motion express parcel positioning.
• This algorithm is suitable for practical implementation in dynamic logistics sorting environments.
• The study highlights the potential of advanced AI techniques in revolutionizing logistics operations.