The Local Contrast Method (LCM) has many advantages for detecting large defect targets in optical components. However, it often suffers from low performance when the defect target is located in a local bright region, which reduces the accuracy of defect detection. Here, we propose a new Neighborhood Vector Principal Component Analysis (NVPCA) method for small defect target detection. The main idea is that each pixel and its 8 neighbors in the damage image are treated as a column vector for the application of any operations, and a 9-dimensional data cube is reconstructed using the vectors of all pixels. The main information of the data cube is concentrated in the first dimension, therein being the principal component analysis (PCA) transform. When the NVPCA image is again processed using the LCM, a substantial image enhancement is obtained. After extraction of the features of the enhanced image, the important statistical information for each defect target, including coordinates, size, area, and energy integral, can be obtained. Because the defect targets are separated using a region-growing method, this method offers excellent precision in the detection of small defect targets with a size of 1 pixel. In addition, the method can detect defect targets located in local bright regions. © 2017 OSA.