Correlational examples for convolutional neural networks to detect small impurities

Abstract Convolutional neural networks have been significantly improving common object detection performances for a long time. However, targets across frames are independently detected in an image sequence, and object detection methods in multiple frames are generally divided into two main stages: object detection in every single frame and feature map association across frames. In this paper, a multi-frame detection framework is proposed to directly detect small impurities in opaque glass bottles with liquor. Specifically, a convolutional neural network trained with correlational examples simultaneously detects and correlates proposals, and then links them selectively to obtain robust detection results under challenging illuminations. Besides, memory costs of patch pairs become extremely large compared with those of patches, thus a sequential training procedure is introduced to relax hardware requirements. Extensive experiments on impurity datasets demonstrate superior performances of multi-frame detection frameworks with convolutional neural networks than traditional single-frame models.