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To address the issues of significant object discrepancies, low similarity, and image noise interference between source and target domains in object detection, we propose a supervised learning approach combined with knowledge distillation. Initially, student and teacher models are jointly trained through supervised and distillation-based approaches, iteratively refining the inter-model weights to mitigate the issue of model overfitting. Secondly, a combined convolutional module is integrated into the feature extraction network of the student model, to minimize redundant computational effort; an explicit visual center module is embedded within the feature pyramid network, to bolster feature representation; and a spatial grouping enhancement module is incorporated into the region proposal network, to mitigate the adverse effects of noise on the outcomes. Ultimately, the model undergoes a comprehensive optimization process that leverages the loss functions originating from both the supervised and knowledge distillation phases. The experimental results demonstrate that this strategy significantly boosts classification and identification accuracy on cross-domain datasets; when compared to the TFA (Task-agnostic Fine-tuning and Adapter), CD-FSOD (Cross-Domain Few-Shot Object Detection) and DeFRCN (Decoupled Faster R-CNN for Few-Shot Object Detection), with sample orders of magnitude 1 and 5, increased the detection accuracy by 1.67% and 1.87%, respectively. [ABSTRACT FROM AUTHOR]

In order to solve the problem of low segmentation accuracy of 3D mesh models, a non-deep learning method based on skeleton extraction is proposed. First, the skeleton of the model is extracted. Then, the skeleton feature points are extracted by analysing the relationship between the skeleton data. The feature point with the smallest distance from the centroid in the same neighbourhood is selected as the segmentation point. Finally, based on the improved region-based algorithm, the segmented skeleton region is obtained with the segmentation points as the seeds, and the corresponding original data is segmented through inverse mapping. The experimental results show that compared with the existing non-deep learning methods, the proposed method can obtain better semantic separation results and segmentation boundaries. [ABSTRACT FROM AUTHOR]