Instance Segmentation Model for Accurate Linear Object Shape Prediction

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  • 線形状物体における高精度な形状予測のためのインスタンスセグメンテーションモデル

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<p>Recent advances in deep learning have dramatically improved the performance of instance segmentation, which is the task of predicting object area in images. However, depending on the shape of the target object, precise detection may still be difficult. For example, linear objects such as Wires still pose challenges for accurate instance segmentation due to their unique characteristics about shape. Therefore, we propose an instance segmentation method to accurately detect linear objects. The proposed method focuses on the characteristics of linear objects: continuity and irregularity of shape. We attempt to accurately detect linear objects by using Smooth Loss, which evaluates continuity, and Edge Enhance Loss, which focuses on the correctness of contours. In addition, we propose an evaluation metrics using the distance between contours to evaluate the accuracy of contour prediction. The proposed instance segmentation method improves by around 12% the average performance of contour prediction on the iShape dataset.</p>

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