Printing roller image salient object detection based on multi-scale feature fusion

Abstract

In this study, aiming at the problem that the surface defect image of the printing roller has different sizes and a large number of defects, a salient object detection method based on stepwise multi-scale feature fusion is proposed, which consists of a stepwise multi-scale feature guidance module, a multi-resolution information fusion module, and a deeply supervised side output. Specifically, the stepwise multi-scale feature guidance module contains two paths, top-down and bottom-up, which strengthen the transmission of multi-scale features in the network, and fuse multi-level features through the multi-resolution information fusion module to suppress the interference of background noise and redundant features. In addition, deeply supervised side outputs are used to eliminate differences caused by background and foreground imbalance. The experimental results on the roller surface defect dataset show that the method can achieve Fmax, MAE of 0.7959, 0.0144, and the detection rate can reach 24.98fps, which has better detection performance than other algorithms.