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Defect detection of automotive injector valve seat based on feature fusion (part 3)

As a result, in the detection of the injector valve seat, the picture needs to be compressed, and the picture size is processed to 800 ×600, after obtaining the unified standard image data, the data enhancement method is used to avoid data shortage, and the model generalization ability is enhanced. Data enhancement is an important part of training deep learning models [3]. There are generally two ways to increase data. One is to add a data perturbation layer to the network model to allow the image to be trained every time, there is another way that is more straightforward and simple,the image samples are enhanced by image processing before training, we expand the data set using image enhancement methods such as geometry and color space, and use HSV in the color space, as shown in Figure 1.

Improvement of Faster R-CNN defect defection model In the Faster R-CNN algorithm model, first of all, you need to extract the features of the input picture, and the extracted output features can directly affect the final detection effect. The core of object detection is feature extraction. The common feature extraction network in the Faster R-CNN algorithm model is the VGG-16 network. This network model was first used in image classification [4], and then it has been excellent in semantic segmentation [5] and saliency detection [6].

The feature extraction network in the Faster R-CNN algorithm model is set to VGG-16, although the algorithm model has a good performance in detection, it only uses the feature map output from the last layer in image feature extraction, so there will be some losses and the feature map cannot be fully completed, which will lead to inaccuracy in detection of small target objects and affect the final recognition effect.