Intelligent Detection Method for Froth Flotation Based on YOLOv5

YOLOv5 based flotation foam intelligent detection method is based on machine vision technology, and uses YOLOv5 based deep learning framework to tackle the problem of data mining of flotation froth. It provides real-time video of the foam, and at the same time extracts real-time status data such as the size and gradation ratio, flow direction, flow rate, and life cycle of the flotation foam for the flotation staff. This provides effective and accurate data support for standardizing and optimizing flotation control. The method is equipped with automatic detection and control reference functions of abnormal working conditions. When the flotation working condition changes, it will automatically prompt the process operators and output the control reference value of the flotation tank liquid level and air intake based on historical data to assist in adjusting the control strategy, thereby improving the response speed of abnormal working conditions. The system helps to stabilize the production and operation rate, improve the stability of flotation process control, and provide strong data support for fine flotation process control in concentrators of mining companies.