Defect detection of automotive injector valve seat based on feature fusion

Abstract

Automobile fuel injector seat is a device that injects gasoline into the automobile cylinder. It plays a very important role in the control of automobile fuel quantity. However, due to the small parts and susceptibility to processing technology, in the production process of automobile fuel injector seats, it is inevitable to leave scratches, defects, rust spots, white spots and other defects. This paper will use the depth detection technology to complete the flaw detection of the injector seat, the depth detection algorithm model Faster R-CNN is improved. Defect detection can be seen that when extracting features, the Faster R-CNN algorithm model only uses the features of the last layer of convolution output, and has a large loss and affects the effect of small target detection. In order to improve the detection ability and solve the problem of missing detection caused by multi-scale and small targets, we introduce the idea of multi feature fusion in the stage of feature network extraction, and compares the improved algorithm model with the original Faster R-CNN model on the data set of injector seat. It is found that the improved model can be better applied to the flaw detection of automobile injector seats.