Laser engraving surface defect monitoring method based on improved YOLOv8
During the laser engraving process,due to factors such as lighting conditions and on-site environment,the apparent features of the tested surface may change,resulting in a decrease in image quality and ultimately affecting the accuracy of defect de-tection.Meanwhile,different materials and carving parameters can lead to the diversity and variability of surface features,which also puts higher demands on the adaptability and generalization ability of defect detection methods.In order to achieve precise iden-tification of surface defects,this study proposes a laser engraving surface defect monitoring method based on improved YOLOv8.Firstly,prepare high-definition cameras,laser engraving machines,and image acquisition equipment,set shooting parameters on the basis of ensuring that the equipment is connected properly,and collect surface images of laser engraving.Secondly,the sampled images are subjected to denoising,brightness contrast adjustment,color scale distribution optimization,and sharpening processing to improve the detail clarity of laser engraved images.AOD PONO Net network is used to complete image dehazing and enhance-ment processing.Finally,based on the conventional YOLOv8 algorithm,PANet structure is added to further fuse feature maps of different scales,obtaining richer feature information in the image.Then,by fusing information from shallow feature maps,surface defect monitoring is achieved.The experimental results show that this method can not only achieve accurate recognition of surface defect images in laser engraving,but also detect the number of surface defects.
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