An improvement and application of a model conducive to productivity optimization

Abstract

The metal defect detection and identification technology have been successfully applied in actual industrial production. But there are still some problems in the steel strip defect detection task, such as the difficulty in data collection and the poor identification effect caused by the small defect target. Aiming at these problems, this paper presents an algorithm based on convolutional neural network for the detection of steel strip small target defects. First, the original image is clipped by the way that moving the slider. At the same time, the feature pyramid structure is increased to conduct feature fusion for the output of the feature extraction part and Group Normalization is used to accelerate the convergence of the model, so that the model can better adapt to the target task, effectively solve the problem of small defect object information loss on the surface of the target steel strip, and enhance the generalization of the model. In order to evaluate and test the validity of the model, a steel strip defect dataset (BS5-DET) is constructed. The experimental results show that the mAP of the model reaches 54.50% on the defect data set of the BS5-DET steel belt, which has a certain application value.