Abstract: The present disclosure provides systems and methods to enable detection of linear objects such as utility poles in complex and heterogeneous outdoor surroundings. The methods deal with shape and orientation as prominent features of a pole model. Candidate trapeziums from 2D images of the face of the poles are extracted, some of which represent parts of the pole. To overcome the missed detection of certain parts due to problems of occlusion and diffusion into background, shape based techniques, that extrapolate and capture a longer trapezium representing the pole is implemented. The region growing stage or extrapolation is driven by orientation-based clustering of trapeziums. Context information is further used to identify objects of interest, by discarding false positives. Besides detecting poles of interest, the detected poles are further analyzed to identify damages, if any.

Abstract: This disclosure relates generally to visual inspection systems, and more particularly to a method and system for facilitating real time detection of linear infrastructural objects in aerial imagery. In one embodiment, a background suppression technique is applied to one or more hardware processors to a HSV image. Further, a mean shift filtering technique is applied to the hardware processors to find a peak of a confidence map and then a gradient image generation is performed for a plurality of edges of the image. A seed point pair along a middle cut portion of a linear feature of the HSV image to identify one or more boundaries of the seed point pair is extracted and then a contour growing approach to detect the boundaries of the linear feature is initiated. Lastly, one or more false positives are removed by using a rigidity feature, the rigidity feature being equivalent to the total sum of gradient orientations.

Abstract: This disclosure relates generally to ge visual inspection systems, and more particularly to a method and system for facilitating real time detection of linear infrastructural objects in aerial imagery. In one embodiment, a background suppression technique is applied to one or more hardware processors to a HSV image. Further, a mean shift filtering technique is applied to the hardware processors to find a peak of a confidence map and then a gradient image generation is performed for a plurality of edges of the image. A seed point pair along a middle cut portion of a linear feature of the HSV image to identify one or more boundaries of the seed point pair is extracted and then a contour growing approach to detect the boundaries of the linear feature is initiated. Lastly, one or more false positives are removed by using a rigidity feature, the rigidity feature being equivalent to the total sum of gradient orientations.

Abstract: The present disclosure provides systems and methods to enable detection of linear objects such as utility poles in complex and heterogeneous outdoor surroundings. The methods deal with shape and orientation as prominent features of a pole model. Candidate trapeziums from 2D images of the face of the poles are extracted, some of which represent parts of the pole. To overcome the missed detection of certain parts due to problems of occlusion and diffusion into background, shape based techniques, that extrapolate and capture a longer trapezium representing the pole is implemented. The region growing stage or extrapolation is driven by orientation-based clustering of trapeziums. Context information is further used to identify objects of interest, by discarding false positives. Besides detecting poles of interest, the detected poles are further analysed to identify damages, if any.