<p>Developing intelligent and automatic systems to detect defects in additive manufacturing parts is still a great challenge. In this work, the binder jetting technology has been used to manufacture molds for aluminum casting, and the defects of the parts obtained in these molds have been analyzed and compared with the parts obtained in molds manufactured with the traditional sand-casting method. The main defects obtained in both casting methods have been due to porosity, which is one of the most critical defects affecting the mechanical behavior of the parts. We propose a methodology to develop an automatic system for detecting these defects in casting parts. We also presented the <i>Porosity Detection Corpus</i>, a novel and publicly available dataset containing 204 pictures taken after cross-sectioning the manufactured parts, 102 for each casting technique. Then, we manually annotated the bounding boxes that include gas and shrinkage pores using two different labeling: only pores and their type. Finally, we trained and evaluated a pre-trained You Only Look Once (YOLO) model on the Porosity Detection Corpus, considering different thresholds in terms of recall. For detecting unique pores, we recommended using 25% of threshold, with a recall of 0.599. For classifying the type of pore, gas or shrinkage, we recommended a threshold of 25% with a mAP of 0.377.</p>