Development of an OpenCV-based Intelligent Object Recognition, Detection and Sorting System for Retail Automation
Kushagra Chaturvedi∗, Samarth Kulkarni, Satvik Chaturvedi, Vinayak Bharadwaj, Dr. Anand Jatti, Dr. Sudarshan BG
Department of Electronics and Instrumentation Engineering, RV College of Engineering, Bengaluru, India
kushagrc.ei22@rvce.edu.in
Department of Electronics and Instrumentation Engineering, RV College of Engineering, Bengaluru, India
samarthk.ei22@rvce.edu.in
Department of Electronics and Instrumentation Engineering, RV College of Engineering, Bengaluru, India
satvikc.ei22@rvce.edu.in
Department of Electronics and Instrumentation Engineering, RV College of Engineering, Bengaluru, India
vinayakb.ei22@rvce.edu.in
Department of Electronics and Instrumentation Engineering, RV College of Engineering, Bengaluru, India
anandjatti@rvce.edu.in
Department of Biotechnology, RV College of Engineering, Bengaluru, India
sudarshanbg@rvce.edu.in
ABSTRACT: This paper presents an end-to-end, information-driven system that links conveyor sensor events to image-based recognition for retail automation. An ultrasonic sensor and microcontroller detect items and halts the belt, triggering a smartphone to capture photos that are uploaded to a processing host with a shared event identifier and timestamp. A lightweight computer-vision pipeline (OpenCV + YOLO) extracts object labels, attributes and bounding boxes; results and raw metadata persisted as structured CSV records. By anchoring image capture to physical events, the system minimizes unnecessary inference, ensures reliable pairing of sensor and image data, and enables immediate analytics, audit trails and human-in-the-loop verification. The approach is practical on common hardware, supports real-time operations, and provides a reproducible dataset for downstream.
KEYWORDS: Object detection, OpenCV, YOLO (real-time detection), Event-driven logging, Ultrasonic sensor (HC-SR04), Conveyor automation, Retail inventory management
