Printing defects are extremely common in the manufacturing industry. Although some studies have been conducted to detect printing defects, the stability and practicality of the printing defect detection has received relatively little attention. Currently, printing defect detection is susceptible to external environmental interference such as illuminance and noise, which leads to poor detection rates and poor practicality. This research develops a printing defect detection method based on scale-adaptive template matching and image alignment. Firstly, the research introduces a convolutional neural network (CNN) to adaptively extract deep feature vectors from templates and target images at a low-resolution version. Then, a feature map cross-correlation (FMCC) matching metric is proposed to measure the similarity of the feature map between the templates and target images, and the matching position is achieved by a proposed location refinement method. Finally, the matching image and the template are both sent to the image alignment module, so as to detect printing defects. The experimental results show that the accuracy of the proposed method reaches 93.62%, which can quickly and accurately find the location of the defect. Simultaneously, it is also proven that our method achieves state-of-the-art defect detection performance with strong real-time detection and anti-interference capabilities.

The existing synthetic aperture radar (SAR) ship datasets have an imbalanced number of inshore and offshore ship targets, and the number of small, medium and large ship targets differs greatly. At the same time, the existing SAR ship detection models in the application have a huge structure and require high computing resources. To solve these problems, we propose a SAR ship detection model named mask efficient adaptive network (MEA-Net), which is lightweight and high-accuracy for imbalanced datasets. Specifically, we propose the following three innovative modules. Firstly, we propose a mask data balance augmentation (MDBA) method, which solves the imbalance of sample data between inshore and offshore ship targets by combining mathematical morphological processing and ship label data to greatly improve the ability of the model to detect inshore ship targets. Secondly, we propose an efficient attention mechanism (EAM), which effectively integrates channel features and spatial features through one-dimensional convolution and two-dimensional convolution, to improve the feature extraction ability of the model for SAR ship targets. Thirdly, we propose an adaptive receptive field block (ARFB), which can achieve more effective multi-scale detection by establishing the mapping relationship between the size of the convolution kernel and the channel of feature map, to improve the detection ability of the model for ship targets of different sizes. Finally, MEA-Net is deployed on the Jeston Nano edge computing device of the 2 GB version. We conducted experimental validation on the SSDD and HRSID datasets. Compared with the baseline, the AP of MEA-Net increased by 2.18% on the SSDD dataset and 3.64% on the HRSID dataset. The FLOPs and model parameters of MEA-Net were only 2.80 G and 0.96 M, respectively. In addition, the FPS reached 6.31 on the Jeston Nano, which has broad application prospects.