Yolo Based Canny Human Body Parameter Estimation for E-Commerce - Lonkar, Shweta K.; M. Joshi, Shubhangi; D. Kale, Swati; Pusdekar, Prashant (Journal of Neonatal Surgery, 2025)
In the rapidly evolving world of e-commerce, accurate human body parameter estimation is essential for enhancing online shopping experiences, particularly in the fashion and apparel industries. This paper presents an advanced system designed to estimate key body parameters using the You Only Look Once (YOLO) algorithm, known for its fast and reliable real-time object detection capabilities. The system efficiently detects and analyzes human body features to measure dimensions such as height, chest circumference, waist size, and limb proportions. By incorporating image preprocessing, feature extraction, and data refinement techniques, the model ensures precise estimations even in varied lighting conditions and diverse poses. The proposed approach addresses challenges in virtual fitting rooms, size recommendation engines, and personalized shopping experiences. Experimental results demonstrate the system’s accuracy, scalability, and potential for seamless integration into e-commerce platforms, thereby improving customer satisfaction and reducing return rates. This work contributes to the advancement of automated body measurement technologies, offering practical solutions for the growing demands of the online retail industry.