A Trajectory-based Attention Model for Sequential Impurity Detection

Abstract

Impurity detection involves detecting small impurities in the liquid inside an opaque glass bottle with complex textures by looking through the bottleneck. Sometimes experts have to observe continuous frames to determine the existence of an impurity. In recent years, region-based convolutional neural networks have gained incremental successes in common object detection tasks. However, sequential impurity detections present more challenging issues than detecting targets in a single frame, because consecutive motions and appearance changes of impurities cannot be captured using those common object detectors. In this paper, we propose a simple and controllable ensemble architecture to alleviate this problem. Specifically, a siamese fusion network is used to generate impurity proposals, then an attention model based on visual features and trajectories is proposed to localize a unique region proposal in each frame, finally, a sequential region proposal classifier using a long-term recurrent convolutional network is applied to refine impurity detection performances. The proposed method achieves 79.81%mAP on IML-DET datasets, outperforming a comparable state-of-the-art Mask R-CNN model.