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The pavement defect detection is challenging due to the diverse defects and their unpredictable formations. Current methods often struggle to perform well in situations with complex pavement backgrounds and weak textures, a problem that arises from the interference of various pavement information. We proposed an unsupervised pavement defect detection method with a multiscale gradient selection-based coupled-hypersphere adaptation (MGSCA) to alleviate these challenges. Specifically, the proposed method first constructs a lightweight and representative feature-capturing memory bank to effectively capture features of complex pavement backgrounds and reduce the inference time. Utilizing these representative features as hypersphere centers, we propose a hypersphere-coupled adaptive module that trains an axis-based self-attention descriptor to reduce the data bias of the pretrained network and improve the defect detection from the nondefective pavement backgrounds. On the Crack500 dataset, the proposed method achieved 0.930 area under the ROC curve (AUROC) in defect detection. For defect localization, the proposed method achieved 0.925 AUROC, 0.791 per-region-overlap (PRO), and 0.744 DICE. Compared with several state-of-the-art methods, the results on three datasets demonstrate the effectiveness of our proposed method in different scenarios.

Robust model fitting is a critical technique for artificial intelligence. The performance of most robust model fitting techniques heavily depends on the use of sampling algorithms. In this paper, we propose an efficient guided sampling algorithm for multi-structure data by using the neighborhood consensus and the residual sorting. Specifically, a Neighborhood Consensus based Strategy (NCS) is first proposed to select the first datum (i.e., seed datum) of a minimal subset, and then a Residual Sorting based Strategy (RSS) samples the rest data of the minimal subset based on the seed datum. This strategy effectively combines the benefits of neighborhood consensus and residual sorting, where neighborhood consensus can judge whether a selected data point is an inlier, and residual sorting encourages this strategy to select data points from the same structure of the first selected data point. Moreover, to achieve better fitting performance, the Markov Chain Monte Carlo process is used to combine NCS with the random selection strategy to select the seed datum, and an appropriate size is set to the initial block of randomly sampled hypotheses for RSS. Experimental results on three vision tasks (e.g., two-view motion segmentation and 3D motion segmentation) demonstrate that the proposed algorithm achieves superior performance to several state-of-the-art sampling algorithms.