**2.2 Catenary detection**

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The overall detected lines are compared with the labeled lines in order to differentiate the positive and negative samples. The positive samples are power lines that overlap the previously labeled lines. The negative samples are other lines detected in the scene that are not power lines. This corresponds to lines that have not been tagged. The overlapping between the tagged lines and detected lines that are not power

After that, a feature extraction stage is performed by using HOG descriptors [16], which are computed for the selected lines on the labeled dataset. This is done in spaced squared windows centred in the lines. In **Figure 5**, the extraction of the

Finally, the obtained descriptor values in the previous stage are the input data for the classifier. The SVM classifier is trained with this input data using a sigmoid

The detection mode has to be used after a training mode. The objective of this

This begins with segmenting and detecting lines as in the previous mode. After that, HOG descriptors are computed across the detected lines using squared windows as were done in training mode. This information is gathered for the classifier.

HOG descriptor in windows across a labeled power line is shown.

stage is to detect the power lines using the previously trained classifier.

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kernel.

**Figure 4.**

lines must be zero.

*Process for line recognition system, training and detection.*

Most of the works on power line detection are focused in straight line detection. Nevertheless, the electrical infrastructure is composed of catenaries which are

#### **Figure 5.** *Extracting HOG descriptor in lines.*

**Figure 6.** *Line detection in the scene.*

**Figure 7.** *Result of a machine learning method.*

**Figure 8.** *Catenary detection based in a segment concatenation.*

generated when a flexible cable is suspended between two poles or towers. This type of object appears in images of electrical infrastructure taken from non-top-down views, which could be obtained using manned aircraft or UAVs. Different methods for catenary detection that includes the use of matching filters, line segment pool and a graph-cut model as is shown in [17] exist, also using geometrical considerations and data structures of segment concatenation [18]. The results of catenary detection based on a segment concatenation are shown in **Figure 8**.
