Abstract: 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% improvement over the YOLOXs, demonstrating the effectiveness of the proposed method in improving the detection of foreign objects on power transmission lines.