Aiming at addressing the challenges posed by complex backgrounds in the inspection process of transmission lines, as well as the poor detection effect resulting from a high number of targets with varying scales and the tendency to overlook or incorrectly detect small target defects, this study proposes a multi-scale defect detection network MC-YOLO for transmission lines using YOLOv8s as the basic architecture. Firstly, the EMA attention mechanism is introduced in the backbone network to enhance spatial feature extraction capability and reduce computational redundancy. Secondly, the bottleneck in the C2f module is replaced by the PKI module, which extracts multi-scale dense texture features between different receptive fields by mimicking the human visual system. Lastly, a Multiscale Sequence Feature Fusion (MSF) structure is constructed to improve the fusion of feature maps from multiscale sequences, and an additional detection layer is added to enhance the fusion capability of shallow and deep feature maps. Compared with the baseline model, the experimental results demonstrate that our proposed detection network improves mAP@0.5 by 6.8%, reduces model computation by 25.4%, and significantly enhances small target detection effectiveness. Compared with several existing target detection methods, our approach offers advantages in terms of accuracy and complexity, better meeting actual inspection needs for transmission line scenarios. The method presented in this paper outperforms several existing target detection methods in terms of both accuracy and complexity while better satisfying actual transmission line inspection requirements.