<p>Small object detection is a critical research area in computer vision, with broad applications in industrial defect detection and satellite remote sensing. In printing defect detection, defects on printed materials are often small, weak in detail, and low in contrast. While mainstream deep learning-based object detection algorithms perform well on conventional objects, they face significant challenges in extracting features for printing defects. Additionally, the small size and blurred visual characteristics of defects make detection results highly susceptible to background interference, leading to a high false positive rate. To address these issues, this paper proposes a progressive printing defect detection method based on contextual information (PCINet). Specifically designed for small defects with unclear visual features, PCINet enhances defect feature representation by reconfiguring the backbone network during the feature extraction phase, thereby improving detection performance. A global semantic reconstruction module is introduced to progressively explore the contextual relationships between defect targets and their surrounding environment. This module includes a global semantic awareness unit, which expands the receptive field and enriches regions of interest, and a regional interaction-assisted reconstruction unit, which refines defect edges and suppresses redundant background interference. Experimental results demonstrate that the proposed method performs well on the printing defect detection dataset, the Printing Defect Dataset 2, and the DOTA-V1.0 dataset. It significantly reduces the false positive rate and exhibits strong robustness in detecting defects under complex backgrounds and low contrast. Furthermore, PCINet shows good generalization capabilities in other small object detection tasks, underscoring its broad application potential.</p>