Insulators find extensive use across diverse facets of power systems, playing a pivotal role in ensuring the security and stability of electrical transmission. Detecting insulators is a fundamental measure to secure the safety and stability of power transmission, with precise insulator positioning being a prerequisite for successful detection. To overcome challenges such as intricate insulator backgrounds, small defect scales, and notable differences in target scales that reduce detection accuracy, we propose the AC-YOLO insulator multi-defect detection network based on adaptive attention fusion. To elaborate, we introduce an adaptive weight distribution multi-head self-attention module designed to concentrate on intricacies in the features, effectively discerning between insulators and various defects. Additionally, an adaptive memory fusion detection head is incorporated to amalgamate multi-scale target features, augmenting the network’s capability to extract insulator defect characteristics. Furthermore, a CBAM attention mechanism is integrated into the backbone network to enhance the detection performance for smaller target defects. Lastly, improvements to the loss function expedite model convergence. This study involved training and evaluation using publicly available datasets for insulator defects. The experimental results reveal that the AC-YOLO model achieves a notable 5.1% enhancement in detection accuracy compared to the baseline. This approach significantly boosts detection precision, diminishes false positive rates, and fulfills real-time insulator localization requirements in power system inspections.

As an external manifestation of human emotions, expression recognition plays an important role in human-computer interaction. Although existing expression recognition methods work perfect on constrained frontal faces, there are still many challenges in expression recognition in natural scenes due to different unrestricted conditions. Face recognition in natural scenes is a problem that the intra-class gap is larger than the inter-class gap. In order to solve this problem, we propose a method of generating a reference expression using GAN and comparing it with the original expression to generate differential features, so as to avoid interference of irrelevant information on expression recognition. Besides, we have specifically optimized the GAN network that generates reference expressions to make the generated reference expression more natural. We used Resnet50-V2 pre-trained on ImageNet to better present the differential features of the original expression and the reference expression. After testing on the two datasets, our model achieves higher accuracy than other models.