The invention provides a method for detecting bird nests in power transmission line poles based on unmanned plane images, which is method of perceiving and analyzing power transmission line structure features. Firstly, line segments in different directions are extracted from a polling image, a Gestalt perception theory is adopted to merge small interrupted line segments, and the merged line segments are clustered into parallel line sets. Then, the image is divided into 8*4 blocks according to a structural feature (nearly symmetrical intersection feature) of a pole in the image, the quantity statistics of line segments in four different directions in each block is analyzed, and an area where the pole is in the image is detected. The invention provides a bird nest detection method which fuses colors and textures. Firstly, an area of color consistency in an image is obtained by mean-shift cluster segmentation. Then, according to features of an H histogram of a bird nest sample, multiple areas which are most similar to the bird nest sample in the image are selected as candidate areas of a bird nest through a histogram interaction method. Then, three co-occurrence matrix features of entropy and inertia moments and dissimilarity, which can best represent the bird nest, are selected to calculate texture features of the candidate areas of the bird nest. Finally, matching between each candidate area of the bird nest and the bird nest sample texture similarity is carried out to achieve the bird nest detection.