To address the common issues of false and missed detections in the current process of foreign object detection on power transmission lines, as well as the limited detection accuracy due to the variability in the size of foreign objects, this paper proposes an AAGV-YOLOX model for the detection of foreign objects on power transmission lines. The model first designs an Adaptive Dilated Convolution (Adaptive Dilated Convolution, ADConv) and constructs a feature extraction module (Adaptive Dilated Convolution Module, ADCM) to effectively distinguish the widely distributed foreign objects from background information, thereby enhancing the model's feature extraction capabilities. Subsequently, an Adaptive Receptive Field Feature Fusion (Adaptive Receptive Field Feature Fusion, ARFFF) module is introduced into the neck network to fully integrate features of different scales, further improving detection accuracy. Finally, the GVFL loss function is proposed, which not only increases the convergence speed of the proposed network but also enhances the localization accuracy. Experimental results show that the average precision mean of this model on the self-built dataset of foreign objects on power transmission lines reaches 90.34%, with a 5.56 percentage points improvement over the YOLOXs, demonstrating the effectiveness of the proposed method in improving the detection of foreign objects on power transmission lines.

Recent StyleGAN-based face swapping methods have been able to generate very realistic high-resolution face swapping results, but they are often plagued by the challenge of maintaining various attributes (such as expression, pose, and illumination). One reason is that these methods usually focus on the latent codes of facial semantic features corresponding to the $\mathcal{W}/\mathcal{W}+$ space, and latent codes in these spaces are often highly entangled. To address this issue, we propose a new method, LDSwap. for disentangling and re-fusing latent codes in the StyleSpace. The semantic-related latent code disentangling module (SLDM) we propose can successfully achieve facial semantic feature exchange and reorganization by disentangling latent codes. In addition, we propose a channel-split adaptive feature fusion module (CAFF) that adaptively learns and refuses spatial information in the target image. This module can learn spatial features from the target image without interference from the features of the target face region. Through qualitative and quantitative evaluation, we demonstrate that LDSwap shows significant improvements over three state-of-the-art methods in maintaining the appearance of semantic features.