结合低光照增强的轻量级瓷砖表面缺陷检测OA

To address the issue of image quality degradation under low-light conditions during tile surface defect detec-tion,which reduces the perception capability of detection networks,this paper proposes a lightweight tile surface defect detection algorithm that combines low-light image enhancement with an improved you only look once version 8 small(YOLOv8s)network.First,an unsupervised low-light tile image enhancement network is designed to im-prove the quality of low-light tile images.This network enhances image brightness through self-calibrated illumina-tion estimation and removes image noise before and after illumination estimation through joint denoising.Then,the YOLOv8s network is lightweighted by designing the cross stage partial fast module integrated with repvit block and efficient multi(C2f-RVB-EMA)module and lightweight shared depthwise separable convolution detection head(LSDSCD)detection head to enhance the feature discrimination ability of the detection model while reducing computational costs,facilitating model deployment.Experimental results show that compared with the original YOLOv8s model without image enhancement,the proposed algorithm improves the mAP@50 metric by 4.1%and reduces the floating-point operations per second(FLOPS)by 47.0%,demonstrating the effectiveness of the pro-posed method.