Envisioning the Future of Supply Chain Transportation Management- Exploring Crowd Density Image Recognition with Deep Learning
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Abstract
Over the past few decades, artificial intelligence (AI) technology has made significant progress, especially in image recognition and natural language processing. Deep learning, as a multi-level artificial neural network architecture, can automatically identify and learn data features through large amounts of data to achieve high-precision prediction and identification. This research aims to explore the practical application of deep learning in the field of image recognition, and designs an automated image recognition system, taking the identification of crowd density in a train carriage as an example. The system can accurately detect the number of passengers in each carriage, thereby optimizing urban rail transit strategies, ensuring even distribution of passengers and providing the best commuting experience. This technology also has important implications for supply chain management. Accurate people flow identification technology can improve resource utilization efficiency, optimize logistics resource allocation, and reduce transportation time and costs through real-time monitoring and prediction of people flow density. It can also improve demand forecasting and analyze historical data through deep learning algorithms to help companies predict demand more accurately and avoid excessive transportation and shared traffic. This technology enhances supply chain transparency and promotes sustainable development by monitoring the entire transportation process in real time. These applications can improve the operational efficiency of the transportation system and significantly improve the quality and efficiency of supply chain management, demonstrating the huge potential of deep learning technology in modern supply chain management.
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