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Related Experiment Video

Updated: Sep 10, 2025

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Real time wire rope detection method based on Rockchip RK3588.

Mengpeng Qian^1,2, Yong Wang³, Shaoqing Liu⁴

¹University of Science and Technology of China, Hefei, 230026, China.

Scientific Reports

|August 20, 2025

Summary

This summary is machine-generated.

This study introduces a real-time wire rope fault detection system for automated coal mines. The Mini-YOLO model on the Rockchip RK3588 platform achieves faster speeds and improved accuracy for edge devices.

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Area of Science:

* Mining Engineering
* Computer Vision
* Edge Computing

Background:

* Automated coal mines require autonomous wire rope fault detection.
* Server-based systems for conventional model training are computationally intensive.
* Resource-constrained edge devices present challenges for real-time defect detection.

Purpose of the Study:

* To develop a real-time wire rope detection system for edge devices.
* To enhance non-destructive wire rope inspection in mechanized coal mines.
* To optimize computational performance on the Rockchip RK3588 platform.

Main Methods:

* Integration of MobileNetV3, Coordinate Attention (CA), and Inner-IoU loss into YOLOv8 framework, creating Mini-YOLO.
* Implementation of a C++ thread pool to accelerate Neural Network Processing Unit (NPU) output.
* Utilization of Docker for simplified environment configuration and deployment.

Main Results:

* Mini-YOLO demonstrated a 2x computational speed increase and 1.2% accuracy improvement over YOLOv8.
* Achieved an inference time of approximately 18.5 ms per image for real-time detection.
* Real-time data storage with periodic cloud backup was implemented.

Conclusions:

* The proposed Mini-YOLO system effectively addresses real-time defect detection challenges on resource-constrained edge devices.
* Synergistic integration and optimization of algorithmic components are key for edge AI in mining.
* The system enables efficient and reliable autonomous fault detection for wire ropes in automated coal mines.