To address the issues of reduced extraction accuracy caused by blurring or noise in power line images collected for non-contact sag measurement, an Improved-Canny algorithm for power line extraction is proposed in this study. The algorithm comprises three components: image preprocessing, power line edge detection, and power line extraction. First, the region of interest is extracted and converted to grayscale to reduce background interference and highlight power line information. Subsequently, the edge detection accuracy is enhanced by integrating Laplacian of Gaussian filtering, the Scharr operator, and the Otsu method. Finally, Hough transform is utilized to extract linear segments of power lines. The performance of the Improved-Canny algorithm is validated through comparative analysis with traditional algorithms and evaluation using peak signal-to-noise ratio and connected component metrics. The proposed algorithm is validated through practical application in sag measurement. It achieves a power line detection accuracy of 95.425% on 459 images, and the calculated sag values from 50 sampled data points has an error rate of 1.46% on average. The Improved-Canny algorithm meets the requirements for power line extraction and sag measurement.