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A DCNN-based Arbitrarily-Oriented Object Detector for Quality Control and Inspection Application

arXiv 2101.07383 ↗First posted 19 Jan 2021cs.CVcited by 0

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Abstract (arXiv)

Following the success of machine vision systems for on-line automated quality control and inspection processes, an object recognition solution is presented in this work for two different specific applications, i.e., the detection of quality control items in surgery toolboxes prepared for sterilizing in a hospital, as well as the detection of defects in vessel hulls to prevent potential structural failures. The solution has two stages. First, a feature pyramid architecture based on Single Shot MultiBox Detector (SSD) is used to improve the detection performance, and a statistical analysis based on ground truth is employed to select parameters of a range of default boxes. Second, a lightweight neural network is exploited to achieve oriented detection results using a regression method. The first stage of the proposed method is capable of detecting the small targets considered in the two scenarios. In the second stage, despite the simplicity, it is efficient to detect elongated targets while maintaining high running efficiency.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 30 of its 34 references that are in the map. First posted on arXiv 19 Jan 2021.

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30 of 34 references.

References · 30

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18 more
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