The current trend in automated optical inspection (AOI) systems employs deep learning models to detect defects on a metal surface. The setback of deep learning models is that they are time-consuming because the images obtained after every lighting adjustment must be used to train the deep learning models again and confirm whether the detection results have improved. To save the time spent using datasets to train deep networks, we proposed a comprehensive assessment score that combines defect visibility, visibility distribution, and overexposure based on the operation principles of convolution neural networks. It can be used to assess whether the training image dataset can improve the defect detection rate of the deep learning model such as You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), and Faster Region-based Convolutional Neural Network (Faster R-CNN) without training defect image datasets. We collected all of the weight combinations with the right prediction results and used linear regression to obtain the optimal weight coefficients. We found that visibility and overexposure had a greater impact on the comprehensive assessment score. We compared the proposed approach with existing image quality assessment methods, including mean absolute error (MAE), peak signal-to-noise ratio (PSNR), universal image quality index (UIQI), natural image quality evaluator (NIQE), perception-based quality evaluator (PIQE), and blind/referenceless image spatial quality evaluator (BRISQUE). The experiment results indicated that our proposed comprehensive assessment score is more correlated to the F2-score of the detection models than the IQA methods by the verification methods of Spearman Rank Correlation Coefficient (SRCC), Pearson Correlation, and Kendall Correlation. Thus, referring to this index during the collection of image data and choosing datasets with the highest score to train the model will produce better detection accuracy.