A New Method Based on Deep Convolutional Neural Networks for Object Detection and Classification

Accurate object detection and classification has a broad application in industrial tasks, such as fabric defect and invoice detection. Previous state-of-the-art methods such as SSD and Faster-RCNN usually need to carefully adjust anchor box related hyper parameters and have poor performance in special fields with large object size/ratio variations and complex background texture. In this study, we proposed a new accurate, robust, and anchor-free method to handle automatic object detection and classification problems. First, we used the feature pyramid network (FPN), to merge the feature maps of different scales of features extracted from a convolutional neural network (CNN), which allowed easy and robust multi-scale feature fusion. Second, we built two subnets to generate candidate region proposals from the FPN outputs. followed by another CNN that determined the categories of the proposed regions from the two subnets.