AI-BASED PLASTIC CONTAINER DEFECT DETECTION ASSEMBLY LINE CONTROLLER USING ESP32

Abstract

Quality control in plastic container manufacturing is a critical process that directly determines product reliability, customer satisfaction, and the economic efficiency of production lines. Conventional manual inspection methods are slow, inconsistent, and prone to human fatigue errors, making them inadequate for the inspection throughput and defect detection accuracy required by modern high-speed plastic manufacturing environments. This project proposes an AI-Based Plastic Container Defect Detection Assembly Line Controller using an ESP32 microcontroller integrated with a camera module (CAM), ondevice AI-powered image processing software, an LCD display, a buzzer, and an IoT communication interface. The system captures real-time images of plastic containers moving along the assembly line using the camera module and processes them through an AI-based defect detection software model running on the ESP32 to identify surface defects, deformations, cracks, discoloration, and dimensional irregularities. Upon detection of a defective container, the system triggers an immediate buzzer alert, updates the LCD with defect classification details, and transmits a defect event report to the remote IoT monitoring platform for quality management review. Non-defective containers pass inspection without interruption to production flow. The proposed system delivers consistent, high-speed, automated quality inspection with real-time IoTbased production quality monitoring, making it suitable for integration into plastic container manufacturing assembly lines of varying scales and production rates.