2,245 research outputs found
Towards Robust Curve Text Detection with Conditional Spatial Expansion
It is challenging to detect curve texts due to their irregular shapes and
varying sizes. In this paper, we first investigate the deficiency of the
existing curve detection methods and then propose a novel Conditional Spatial
Expansion (CSE) mechanism to improve the performance of curve text detection.
Instead of regarding the curve text detection as a polygon regression or a
segmentation problem, we treat it as a region expansion process. Our CSE starts
with a seed arbitrarily initialized within a text region and progressively
merges neighborhood regions based on the extracted local features by a CNN and
contextual information of merged regions. The CSE is highly parameterized and
can be seamlessly integrated into existing object detection frameworks.
Enhanced by the data-dependent CSE mechanism, our curve text detection system
provides robust instance-level text region extraction with minimal
post-processing. The analysis experiment shows that our CSE can handle texts
with various shapes, sizes, and orientations, and can effectively suppress the
false-positives coming from text-like textures or unexpected texts included in
the same RoI. Compared with the existing curve text detection algorithms, our
method is more robust and enjoys a simpler processing flow. It also creates a
new state-of-art performance on curve text benchmarks with F-score of up to
78.4.Comment: This paper has been accepted by IEEE International Conference on
Computer Vision and Pattern Recognition (CVPR 2019
A DCNN-based Arbitrarily-Oriented Object Detector for Quality Control and Inspection Application
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
- …