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Faint defects such as shallow scratches and dents on the surface of battery metals pose challenges in terms of low contrast and difficulty in distinguishing them from the background texture in traditional 2D images, leading to decreased detection rates. To address these issues, an enhanced detection method based on multi-view fusion for faint defect identification is proposed. To tackle the problem of missing 3D information of faint defects synthesized under different lighting directions, eight images with different lighting angles are captured using a multi-directional lighting device to augment the photometric information of the metal surface. The improved eight-directional photometric stereo simplification model is employed to obtain the 3D information of the metal surface, highlighting the three-dimensional 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 correlation are extracted and fused using fusion coefficients to generate an enhanced image, thereby improving the contrast of faint defects. Experimental results demonstrate that the proposed method achieves a 19.8% improvement in detection accuracy and an 18.9% improvement in recall rate in the detection of actual metal surface defects, effectively addressing the low contrast issue in the detection of faint defects in metal surface images.