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Image-based power line detection is highly important for threat avoidance when the aerial vehicles fly in low altitude. However, it is very challenging for the requirements of high detection rates, low false alarms and real-time application. In this paper, a sequential local-to-global power line detection algorithm is proposed. In the local criterion, a line segment pool is detected by morphological filtering an edge map image, which is computed based on matched filter (MF) and first-order derivative of Gaussian (FDOG). It results in over detection to guarantee high detection rates. In the next global criterion, grouping the line segments into whole power lines is formulated as a graph-cut model based on graph theory. The principal advantage of the proposed algorithm is that it can detect not only the straight power lines but also the curve ones. Experimental results demonstrate that the algorithm has good performances both in detection accuracy and in processing time.