Abstract: The engraved surface of automotive engine has the functions of carrying engine information,searching for lost,and preventing unauthorized disassembly and modification of the engine.The quality of the engraved surface will directly determine whether the vehicle can be registered and driven normally.However,in the field of automobile manufacturing,manual visual inspection is mainly used for defect detection of engraved surfaces at present,which poses a risk of missed detection.Although there have been some studies on surface defect detection in the industry,they cannot fully adapt to the defect detection of engine engraved surface,which can easily lead to false positives and false negatives.In order to innovate the method of detecting engine engraved surface defects,this paper proposes a defect detection method based on generative data augmentation and improved Faster-RCNN.Firstly,a method for generating engraved surface defect images based on stable diffusion model is proposed to address the problem of limited samples of engine engraved surface defects.This method controls the location of defect generation and restores the character features of the image through a dual mask image,thereby completing the generation of engraved surface defect images and achieving data augmentation of the dataset.Secondly,a synchronous bidirectional fusion feature pyramid network(SBFFPN) is proposed to replace the feature pyramid network(FPN) used in the original algorithm,enhancing the multi-scale feature fusion capability and solving the problem of wide target scale range of engraved surface defects.The experimental results show that the proposed method achieves mAP of 97.52% in detecting engine engraved surface defects,which is 34.73% higher than the original Faster RCNN model and can meet the detection requirements of engine engraved surface defects.

Key words: Defect detection, Image generation, Data augmentation, Faster-RCNN, Multi-scale feature fusion