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A TECHNIQUE FOR OPTIMIZING ORE LOADING PROCESS BY AUTOMATED WAGON IDENTIFICATION AND ASSESSMENT OF ORE PROFILE INSIDE THE WAGON

IP.com Disclosure Number: IPCOM000249055D
Publication Date: 2017-Jan-31
Document File: 7 page(s) / 221K

Publishing Venue

The IP.com Prior Art Database

Abstract

A technique for optimizing ore loading process by automated wagon identification and assessment of ore profile inside the wagon is proposed. The technique includes steps of modeling, camera calibration, wagon model detection and ROI estimation, and a cloud of points estimation. Following components are considered for modeling: a train track, a train running over the train track, and a patch with known distance from the ground and from the track. A chessboard patch is used for camera calibration and as a reference for the world coordinate system. An important consideration of the proposed technique is that different train car models, with different sizes, have observable features at different but known spots in an image. By analyzing these spots over time, the technique detects presence of a train car, identifies the model and computes speed of the train in a fast and robust way. Once the train car model is identified, the technique determines the ROI comprising the inner part of the detected train car. Subsequently, the technique uses stereo pair of images to identify matching points inside the ROI. The matching points are used to compute a cloud of points inside the train car, and estimate the ore load profile.

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A TECHNIQUE FOR OPTIMIZING ORE LOADING PROCESS BY AUTOMATED WAGON IDENTIFICATION AND ASSESSMENT OF ORE PROFILE INSIDE THE WAGON

BACKGROUND

 

The present disclosure relates generally to iron ore loading process and more particularly to a technique for optimizing ore loading process by automated wagon identification and assessment of ore profile inside the wagon.

Generally, the process of ore loading on ore load train terminals is time demanding and prone to error. The ore loading process also requires a human operator to manually control the loading of ore in train cars passing by at slow speed. Profile of the iron load inside the wagon is a key feature since mining companies desire to fill the wagon up to its maximum capacity, without dropping any ore outside the wagon and ensuring the load inside is balanced. The balance is important to prevent harm to the wagon and derailment.

Nowadays, operators pre-define a sequence of train cars that are used for ore loading, and configure the loading supervisor system to execute the loading process, which involves visually controlling the ore loading process to derive good ore load profiles relying uniquely on their experience and skill. Besides demanding additional efforts of the operator, such conventional method introduces risks in the loading process with respect to different optimal ore load profiles of different train car models, operator experience of different train car models and ore loading, and unbalanced loads causing harm to wagons and derailments. When such situations occur, the operator has to rely on her/his experience and intuition to solve them and minimize their impact on the loading process. Some hardware solutions, such as optical sensors are available to estimate the profile of wagon loads, but they are expensive, thereby hindering their wide use.

One conventional technique uses a camera to determine train track characteristics, such as curvature and slope. Another conventional technique assesses in real time railway disaster vulnerability using video computational analytics based on neural networks. Yet another conventional technique identifies vehicles by feature detection and tracks them using a single video camera. However, none of the conventional techniques optimize iron ore loading process by automated wagon identification and assessment of ore profile inside the wagon. Also, most algorithms for visual tracking depend on the motion pattern and the environment observed in the scene and loss of information caused by the projection of the three dimensional (3D) world on a two dimensional (2D) image, noise in images, complex object motion, occlusions, illumination changes, and other scene specific problems render such algorithms unsuitable for use in ore loading process.

It would be desirable to have an improved technique for optimizing ore loading process.

BRIEF DESCRIPTION OF DRAWINGS

Figure 1 depicts a model used by the proposed technique which includes a train track, a trai...