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While general object detection has seen tremendous progress, localization ofelliptical objects has received little attention in the literature. Ourmotivating application is the detection of knots in sawn timber images, whichis an important problem since the number and types of knots are visualcharacteristics that adversely affect the quality of sawn timber. Wedemonstrate how models can be tailored to the elliptical shape and therebyimprove on general purpose detectors; more generally, elliptical defects arecommon in industrial production, such as enclosed air bubbles when castingglass or plastic. In this paper, we adapt the Faster R-CNN with its RegionProposal Network (RPN) to model elliptical objects with a Gaussian function,and extend the existing Gaussian Proposal Network (GPN) architecture by addingthe region-of-interest pooling and regression branches, as well as using theWasserstein distance as the loss function to predict the precise locations ofelliptical objects. Our proposed method has promising results on the lumberknot dataset: knots are detected with an average intersection over union of73.05%, compared to 63.63% for general purpose detectors. Specific to thelumber application, we also propose an algorithm to correct any misalignment inthe raw timber images during scanning, and contribute the first open-sourcelumber knot dataset by labeling the elliptical knots in the preprocessedimages.

Ellipse Detection and Localization with Applications to Knots in Sawn Lumber Images