Ore mud pie target detection method and system based on weak supervision YOLO model

Abstract

The invention discloses an ore mud pie target detection method based on a weak supervision YOLO model, and the method comprises the steps: collecting an ore mud pie image on a conveying belt in real time, and inputting the collected ore mud pie image into a trained WS-YOLO model, so as to obtain a mud pie target in the ore mud pie image. The WS-YOLO model comprises a DarkNet53 network, an FPN network, a first full connection layer and a second full connection layer which are connected in sequence. The target classifier and the target position regression model are connected with the second fullconnection layer, and the active learning module is connected with the target classifier and the target position regression model, the active learning module comprises a US strategy sub-module, an expert labeling sub-module and a sample pool which are connected in sequence, and the output of the sample pool is connected to the input of the DarkNet53 network. According to the method, the problemsof large workload, high cost, long period and the like caused by the fact that a large number of samples need to be accurately labeled in an existing mud pie target detection method can be solved, andthe transplantability of the model among different mines is improved.

Description

technical field [0001] The invention belongs to the technical field of ore detection and computer vision, and more specifically relates to a method and system for detecting ore mud ball targets based on a weakly supervised YOLO model. Background technique [0002] Clay is the main impurity in bauxite ore. If these clays flow into the beneficiation process, a larger dose of chemical reagents (such as alkali, etc.) will be required for desiliconization and calcium removal, which not only increases production costs, but also brings environmental pollution. At the same time, the cement ball has high viscosity and strong deformation resistance, which is easy to cause blockage of production equipment and affect the stability of production. Therefore, effectively removing the mud mass in the bauxite can save energy, reduce emissions, reduce environmental pollution, and reduce costs. [0003] In order to realize the automatic removal of mud balls in bauxite ore, it is first necess...

AI Technical Summary

Problems solved by technology

[0005] In view of the above defects or improvement needs of the prior art, the present invention provides a method and system for ore mud mass target detection based on the weakly supervised YOLO model. A large number of samples lead to heavy workload, high cost, and long technical problems, as well as the poor portability of existing models between different mines

Images