Conveyor belt carrier roller detection method based on YOLOv3

Abstract

The invention relates to a conveyor belt carrier roller detection method based on YOLOv3. The method belongs to the field of computer vision and deep learning. The method comprises the following steps: replacing a feature extraction network darknet53 of YOLOv3 with a lightweight feature extraction network Mobilenet; and frame loss and center loss in the YOLOv3 loss function are replaced with GIOUloss, and an improved YOLOv3-Mobilenet belt carrier roller detection model is constructed. And training the model on a training set, testing the performance of the model on a test set, and comparing the performance test result with the performance of other models. The target recognition method provided by the invention is strong in generalization ability, realizes effective detection of the carrier roller, provides effective guarantee for subsequent judgment of whether the belt is separated from the track and monitoring of the running state, reduces the parameter calculation amount, and improves the speed and accuracy of an original YOLOv3 target detection model.

Description

Technical field [0001] The invention belongs to the field of computer vision and deep learning, and particularly relates to a method for detecting the running state of a conveyor belt idler roller. Background technique [0002] The roller is an important part of the conveyor belt. The main function is to support the weight of the conveyor belt and the material. Due to the high loss and failure rate of the conveyor belt roller, the roller is prone to deformation and causes greater safety hazards; for a long time, the failure of the roller depends on manual labor. The patrol inspection has high work intensity and serious missed inspections, and it is impossible to effectively monitor its operating status; therefore, the realization of the intelligent detection of the conveyor belt rollers is of great significance for the subsequent fault diagnosis of the rollers and the intelligent transformation of the production line. [0003] At present, the mainstream target detection methods bas...

AI Technical Summary

Problems solved by technology

The methods based on candidate frames mainly include R-CNN, Fast-RCNN, Faster-RCNN and other methods. Although these methods have high detection accuracy, they have a large amount of calculation and slow detection speed, so they cannot detect targets in real time; regression-based target detection methods , there are mainly two series of methods, SSD and YOLO. Although the SSD algorithm extracts features from different sizes, it does not consider the relationship between different sizes. Feature fusion can predict the category and location of the detected object at the same time, but YOLOv3 has a low detection accuracy for small objects, and the training parameters are large, which consumes computing resources and cannot be directly used on mobile and embedded terminals.

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