Defect detection in images is a current task in quality control and is often integrated in partially or fully automated sys- tems. Assessing the performances of defect detection algorithms is thus of great interest. However, because this is application- and context-dependent, it remains a difficult task. We describe a meth- odology to measure the performances of such algorithms on large images in a semi-automated defect inspection situation. Consider- ing standard problems occurring in real cases, we compare typical performance evaluation methods. This analysis leads to the con- struction of a simple and practical receiver operating characteristic (ROC) based method. This method extends the pixel-level ROC analysis to an object-based approach by dilating the ground truth and the set of detected pixels before calculating the true-positive and false-positive rates. These dilations are computed thanks to the a priori knowledge of a human-defined ground truth and gives to true-positive and false-positive rates more consistent values in the semi-automated inspection context. Moreover, the dilation process is designed to be automatically suited to the object's shape in order to be applied on all types of defects without any parameter to be