Real-Time Load Monitoring of Logistics Delivery Vehicles Using Deep Learning-Based Image Analysis

In the modern logistics industry, the rapid growth of e-commerce has made real-time load monitoring of delivery vehicles a critical factor in ensuring transportation efficiency and safety.However, traditional load monitoring methods are often hindered by delayed data acquisition and insufficient accuracy, making them inadequate for the high demands of efficient and precise logistics operations.Recently, with advancements in deep learningbased image analysis, image-based load monitoring methods have gained attention.However, existing studies face challenges in robustness and real-time performance, particularly in dynamic and complex environments.To address these issues, this paper proposes a real-time load monitoring method for logistics delivery vehicles based on deep learning techniques, focusing on three core technologies: subpixel edge detection in 2D images, interpolation between consecutive image frames, and real-time load volume calculation.This research aims to enhance the accuracy and real-time capabilities of load monitoring, thereby advancing the intelligent development of the logistics industry.