Weighted ensemble networks for multiview based tiny object quality assessment. (23rd January 2021)

• Record Type: Journal Article
• Authors: Zhou, Yichao; Wu, Wanyin; Zou, Jie; Qiao, Jianwang; Cheng, Jun
• Is Part Of: Concurrency and computation. Volume 33:Number 6(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-01-23
• Subjects: ensemble learning -- image quality assessment -- neural networks -- tiny object quality assessment; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5995
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: As demand for intelligent manufacturing continues to grow, tiny object quality assessment (TOQA) is becoming increasingly importance in industrial automation. Recently, visual‐based TOQA has attracted an increasing attention, since the physical appearance is the foremost assessment index for evaluating the tiny object quality. It is exhausted and challenging to determine the quality of tiny object by manual visual inspection, and thus some machine vision systems are developed for automatic TOQA. Existing systems often use a limited number of cameras to capture the image of fallen tiny object, and thus may be not reliable since the tiny object may be unsound (such as cracked or damaged) in an invisible side. In this article, we develop a novel system for automatic TOQA that captures images of tiny object from multiple (more than two) view points, and propose a novel method termed weighted ensemble network (WENet) to effectively integrate the information of different views. In particular, convolutional neural networks (CNNs) are adopted to extract features from the images of different views. Then the multiview features are weighted combined for tiny object quality prediction. Traditional ensemble approaches usually directly applying average or voting to the prediction results of different views, or learn fixed weights to combine the results. Different from these approaches, the weights are adaptively determined in our method according to the quality of the captured image, since the features extracted from a low‐quality (e.g., blurred) image should contribute less to the final prediction. Handcrafted features and deep features are integrated in a sophisticated way in our method, and we empirically demonstrate the effectiveness of our method on grain quality assessment by investigating different CNN architectures for feature extraction and comparing with the conventional ensemble approaches.