John Billingsley - Mechatronics and Machine Vision in Practice 4
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This Springer imprint is published by the registered company Springer Nature Switzerland AG
The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
These are selected and revised papers from the 26th Annual Conference on Mechatronics and Machine Vision in Practice, held in Toowoomba December 25, 2019.
In the process of selecting the papers, many were rejected as lacking the essential in practice element. The survivors have been grouped into those that deal predominantly with vision and optical sensing, those that relate to agricultural applications, more general robotics and devices, sensing methods and actuation, and finally industrial processes and products.
The conference was held with the hospitality of USQs McGregor College and its Centre for Agricultural Engineering. The local team presented several keynotes not included here, which are likely to be published elsewhere later.
There are strong contributions from a number of Chinese and New Zealand Universities, together with contributions from the Philippines, Emirates, and Germany.
For more details on the topics, there are summaries in each of the part headings, covering a vast array of themes ranging from deep learning applied to vision analysis to a robotic mechanism used for collecting camel dung.
The first chapter is more about sensing than vision, concerned with detecting force at a surgical needle-tip to identify the tissue layer that it has reached. Light is the chosen communication medium, being unaffected by the intense magnetic fields of a MRI environment.
The next chapter concerns the build-up of detritus on a train bogy. Structured light is used to measure its volume.
At a much smaller scale, vision is used to measure the breaking of spores that are used in Chinese medicine. These spores must be broken to act more efficiently.
The fourth chapter in this part concerns the detection of the pose of an object that lacks texture features, for the purpose of grasping it. Although deep learning features in the method, there is a strong experimental basis.
Needle insertion is a common surgery in a variety of procedures such as interventional radiology [].
In the process of spinal epidural puncture anesthesia, the operator will inject anesthetics into the spinal epidural space between the spinal nerve and the yellow ligament through an injection needle (the width range is only about 27 mm, and the distance from the subcutaneous is about 2090 mm). This operation requires the operator to determine the position of the needle tip immediately when the needle tip punctures the yellow ligament into the epidural space and stop the puncture. If the puncture continues, the spinal nerve will be easily punctured, resulting in short-term or lifelong postoperative low back pain, and even paralysis in severe cases []. Therefore, how to capture tactile perception to accurately identify the position of needle tip has been an urgent problem in clinical practice.
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