Abstract: The combination of power intelligent inspection and edge computing plays a crucial role in the construction of power Internet of Things and transparent power grid. However, the low computing power of edge devices leads to the low speed of models running on edge devices, and the low memory of edge devices also limits the memory usage of object detection models. Aiming at the above problems, we put forward a method for transmission line defect edge intelligent inspection based on re-parameterized YOLOv5. First, R-D block and re-parameterized spatial pyramid pooling (SPP) are utilized to improve YOLOv5, which adopt the re-parameterization technology to enhance the inference speed, and re-parameterized YOLOv5 is developed hereby. In addition, ResRep is used to perform channel pruning on the model, decreasing the memory usage of the model. Finally, the model is deployed to NVIDIA Jetson Xavier NX embedded platform, and C++ language combined with TensorRT graph optimization is employed to optimize and accelerate the model, further reducing the inference latency and memory usage. The experimental results show that the proposed method is five times the inference speed and forty-one percent the memory cost of YOLOv5, with accuracy being improved by 2.4%, therefore, the efficiency of transmission line edge intelligent inspection can be significantly improved.