为了解决小样本目标检测算法中网络倾斜现象，小样本数据误检、漏检等问题，提出一种基于小样本目标检测的配电线路异物识别方法。首先，通过在迁移训练中引入注意力机制，解决网络倾斜现象。其次，提出在线难度样本选择的方法，解决小样本分类以及小尺寸异物误检和漏检问题。再次，采用内卷积解决传统卷积问题，提高异物检测精度。再次，提出新的锚框方案，解决小尺寸异物目标定位不准问题。最后，构建一个配电线路异物数据集。该方法相较于之前先进算法在配电线路异物检测数据集上检测精度提高了4.4%，达到98.6%，具有优异性能。

In order to address issues such as network bias, false positives, and false negatives in few-shot object detection algorithms, a method for foreign object detection in distribution lines based on few-shot object detection was proposed. Firstly, an attention mechanism was introduced during transfer training to mitigate network bias. Secondly, a method for online hard sample selection was proposed to tackle the problems of few-shot classification, as well as false positives and false negatives for small-sized foreign objects. Thirdly, internal convolution was employed to address traditional convolution issues, thereby improving foreign object detection accuracy. Fourth, a novel anchor box scheme was introduced to address the issue of imprecise localization of small-sized foreign objects. Finally, a distribution line foreign object dataset was constructed. Compared to previous state-of-the-art algorithms, this method demonstrates a 4.4% improvement in detection accuracy on the distribution line foreign object detection dataset, reaching 98.6%, with outstanding performance.