Optimizing the Shopping Experience Through Smart Trolleys with Integrated Deep Learning Techniques

This research study presents an innovative smart shopping trolley system designed to enhance the retail shopping experience by integrating advanced deep learning techniques and sensor technology. The system begins with a secure membership card login process, enabling personalized customer interaction. Upon entry into the trolley system, products can be easily added and identified through a camera utilizing EfficientN et, a deep learning model trained on the store's inventory. The system incorporates an infrared motion sensor and a weight sensor to ensure accuracy in product registration, enhancing reliability and preventing errors. As the shopping process unfolds, a Transformer-based recommendation system analyzes real-time choices and shopping history to provide tailored product suggestions displayed on the trolley's LCD screen. To enhance security, an Autoencoder neural network monitors for anomalies in product identification and removal, alerting store personnel in case of discrepancies. Upon completing the shopping process, a QR code is generated for payment, streamlining the checkout experience. This smart shopping trolley system optimizes the shopping process, leveraging real-time data to improve efficiency, security, and overall customer satisfaction in the retail environment. The system aims to revolutionize traditional shopping methods by applying deep learning and IoT technologies and create a seamless, engaging consumer experience.