Insulator Multi-Defect Detection Based on Multi-Dimensional Cooperative Interaction

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.