  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.
