Since the Convolutional Neural Network (CNN) has surfaced and fascinated the world, many researchers have exploited CNN for image classification, object detection, semantic segmentation, etc. However, the conventional CNNs have a pyramidal structure and were designed to process images which have the same size. Although some CNNs can accept images of various sizes, performance is degraded for images smaller than the size of images used for training. In this paper, we propose MarsNet, a CNN based end-to-end network for multi-label classification with an ability to accept various size inputs. In order to allow the network to accept such images, dilated residual network (DRN) is modified to get higher resolution feature maps, and horizontal vertical pooling (HVP) is newly designed to efficiently aggregate positional information from the feature maps. Furthermore, multi-label scoring module and threshold estimation module are employed to serve the purpose of multi-label classification. We verify the effectiveness of the proposed network through two distinctive experiments. We first verify our model by inspecting and classifying multiple types of defects occurred in PCB screen printer using solder paste inspection (SPI) datasets. Secondly, we verify our network using VOC 2007 dataset. Our network is pioneering in that no research has attempted to accomplish multi-label classification for defects in addition to being able to take input images of various sizes in SPI field. INDEX TERMS Convolutional neural networks, images of various sizes, multi-label classification, printed circuit board, solder paste inspection.

In the field of surface mount technology (SMT), early detection of defects in production machines is crucial to prevent yield reduction. In order to detect defects in the production machine without attaching additional costly sensors, attempts have been made to classify defects in solder paste printers using defective solder paste pattern (DSPP) images automatically obtained through solder paste inspection (SPI). However, since the DSPP images are sparse, have various sizes, and are hardly collected, existing CNNbased classifiers tend to fail to generalize and over-fitted to the train set. Besides, existing studies employing only multi-label classifiers are less helpful since when two or more defects are observed in the DSPP image, the location of each defect can not be specified. To solve these problems, we propose a dual-level defect detection PointNet (D 3 PointNet), which extracts point cloud features from DSPP images and then performs the defect detection in two semantic levels: a micro-level and a macro-level. In the micro-level, a type of printer defect per point is identified through segmentation. In the macro-level, all types of printer defects appearing in a DSPP image are identified by multi-label classification. Experimental results show that the proposed D 3 PointNet is robust to the sparsity and size changes of the DSPP image, and its exact match score was 10.2% higher than that of the existing CNN-based state-of-the-art multi-label classification model in the DSPP image dataset.