In this paper, we propose a garbage classification model that integrates the attention mechanism and multiple network optimization methods. First, we construct a four-category primary network for recyclable garbage, kitchen garbage, hazardous garbage, and other garbage. And then, four secondary networks are constructed to map the above 4 primary categories to 40 secondary classes. Both the primary and secondary networks take Resnet101 as the main backbone network and integrate attention mechanism, Focal loss function, and warm-up learning rate. The experimental results prove that the proposed model has a high classification performance for the HUAWEI cloud garbage classification dataset.
Estimating a six-degree-of-freedom pose from a set of correspondences remains a popular solution for 3D point cloud registration. The random sample consensus (RANSAC) method is a typical pose estimator for this task. However, RANSAC still suffers from several limitations including low efficiency and the sensitivity to high outlier ratios. To tackle these problems, we propose a 1-point sample consensus method. It first constructs a local reference frame for the keypoint based on multi-scale normal vectors, which allows our method to exhibit a linear time complexity. Then, we propose a novel hypothesis evaluation method that concentrates on accurate inliers and is more reliable for hypothesis evaluation. With comparisons with two RANSAC-like methods, our method manages to achieve more accurate and efficient registrations, making it a good gift for practical applications.
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