Enhanced Method for Faint Defects Detection Based on Multi-View Fusion
There are faint defects such as shallow scratches and dents on the surface of battery metals,resulting in low contrast and difficulty to distinguish them from background texture in traditional 2D images,and reducing detection accuracy.To address these issues,an enhanced detection method based on multi-view fusion for faint defect identification is proposed.Aimed at the missing 3D information synthesized by faint defects in different lighting resource directions,eight images with different lighting source angles are used to increase the photometric information of the metal surface through multiple lighting resource devices.The 3D information of the metal surface is obtained through the improved eight-directional photometric stereo simplification model,highlighting the three-di-mensional characteristics of the defects.To address the issues of image blurring and low contrast of faint defects in depth images,the angle sensitivity of height features of faint defects is analyzed.The depth-related 3D information component maps with high correla-tion are extracted and fused by using the fusion coefficients to generate an enhanced image,thereby improving the contrast of faint de-fects.Experimental results show that the proposed method increases the detection accuracy by 19.8％and the recall rate by 18.9％in the detection of actual metal surface defects,effectively achieving the low contrast issue in the detection of faint defects in metal sur-face images.
metal surfacemachine visionphotometric stereo visionimage fusionimage enhancement
NETL
NSTL
万方数据
