Work

Development of a real-time excavator cycle time detection system using YOLO for mining operations: a company case study

Year: 2025

Type: article

Abstract: Productivity in mining operations is highly dependent on the efficiency of heavy equipment such as excavators. Conventional methods for measuring excavator cycle time are often manual and inefficient. This study aims to develop a real-time cycle time detection system using the You Only Look Once (YOLO) object detection algorithm. Video data from excavator operations were annotated to train the YOLO model, which was then integrated into a user-friendly application using OpenCV. The system achieved a mean Average Precision (mAP) of 94.2% and operated at 30 Frames Per Second (FPS), enabling accurate and real-time detection of excavator activities. The system enhanced monitoring efficiency and operational productivity. Its implementation in mining environments demonstrates the potential for automated cycle time analysis to support equipment management, improve safety, and reduce operational delays.

Source: Jurnal POLIMESIN

DOI: 10.30811/jpl.v23i3.6402

Authors: Setia Abikusna, Amadeus Renjiro Wang, Leo Setiawan, Nur Rofiq Syuhada, Randy Putra Afani

Institution: Politeknik Astra

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Responsible consumption and production

Open Access status: diamond

OpenAlex ID: W4413263943

FWCI:

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