Md Tahseen Anam
I am a machine learning enthusiast and currently working at H&M as a machine learning engineer. I have done my master's thesis at Husqvarna AB on machine learning and computer vision. Before that I have worked as a software engineer for three years and developed web application using python web-frameworks.
Sessions
Live stream: https://www.youtube.com/watch?v=rqy3OZn4y-4
The cutting efficiency of a chainsaw is related to the hardness of the wood, For example, it is affected by the existence of knots (hard structure areas) and cracks (no material areas). The current practice involves clean cuts by avoiding knots and cracks. Therefore estimating the relative wood hardness by identifying the knots and cracks beforehand can significantly automate the process of regulating the chain properties, e.g., consumed power, force, etc., which in turn improves the chain's efficiency.
In this talk I will share how I have implemented Mask-RCNN to identify and segment defects in wood cuts and how the result can be used to understand wood hardness to improve cutting efficiency of chainsaw.