Aiming at the problems of small target, large scale difference, multiple types and complex background in the multi-defect image detection of overhead insulators of transmission lines, a multi-dimensional collaborative feature interaction algorithm for multi-scale defect detection of overhead insulators was proposed. Firstly, in the process of multi-scale feature fusion, a multi-dimensional collaborative attention module is introduced to enhance the feature interaction between spatial dimension and channel dimension, so as to enhance the global perception ability of the model for small targets. Secondly, a global receptive field spatial pooling pyramid fast module was constructed to help the network suppress the interference of complex background and aggregate insulator defect features from different receptive fields. Finally, by designing the MG-Detect detection head, the missed detection of small targets was reduced, the adaptability of the algorithm to multi-scale targets was enhanced, and redundant calculations were reduced. Experimental results show that the [email protected] accuracy of the proposed algorithm reaches 97.0%, which is 5.1 % higher than the baseline model. At the same time, the AP of insulator fracture, flash and self-explosion defects increased by 5.1 %, 9.9% and 1.1 %, respectively. The proposed algorithm can more accurately identify the characteristics of insulator defects at different scales.

The development of digitized humanity information provides a new perspective on data-oriented studies of history. Many previous studies have ignored uncertainty in the exploration of historical figures and events, which has limited the capability of researchers to capture complex processes associated with historical phenomena. We propose a visual reasoning system to support visual reasoning of uncertainty associated with spatio-temporal events of historical figures based on data from the China Biographical Database Project. We build a knowledge graph of entities extracted from a historical database to capture uncertainty generated by missing data and error. The proposed system uses an overview of chronology, a map view, and an interpersonal relation matrix to describe and analyse heterogeneous information of events. The system also includes uncertainty visualization to identify uncertain events with missing or imprecise spatio-temporal information. Results from case studies and expert evaluations suggest that the visual reasoning system is able to quantify and reduce uncertainty generated by the data.