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.
Wood cutting properties for the chains of chainsaw is measured in the lab by analysing the force, torque, consumed power and other aspects of the chain as it cuts through the wood log. One of the essential properties of the chains is the cutting efficiency which is the measured cutting surface per the power used for cutting per the time unit. These data are not available beforehand and therefore, cutting efficiency cannot be measured before performing the cut. The efficiency of the chainsaw is closely related to wood hardness and defects like knots and cracks are very important properties when measuring wood hardness. Mask-RCNN is a widely used machine learning model in computer vision that is used to perform instance segmentation. In my work Mask RCNN was used to identify each instance of knots and cracks in a wood cut and then the instance information was used to understand wood hardness. OpenCV was used to perform image processing, open source platform tensorflow and libraries like sklearn, matplotlib, numpy were used to implement the model, perform the tasks and visualise the result. Therefore, I think it can interest other python and machine learning enthusiasts to know about my work.
Speakers: Md Tahseen Anam