Résumé: As a critical component of ensuring the safe and stable operation of trains, the detection of bird's nests on the rail catenary has always been essential. Low-resolution images and the lack of labelled data, however, make it difficult to detect smaller bird's nests (those occupying small pixels in the input image). Previous solution relies on manual online patrol or offline video playback, which severely limits the detection efficiency. Previously, this challenge was addressed by manual online patrol or offline video playback, which severely limits detection efficiency. We propose in this work a context-guided coarse-to-fine detection model (CG-CFDM) for solving the bird's nest detection problem. This solution consists of a context reasoning module and a coarse-to-fine detection network. By detecting domains and matching templates, the context reasoning module generates new labelled context bounding boxes, thereby reducing the burden of annotation. As a result of its delicately designed architecture and powerful representation learning ability, this trained coarse-to-fine detection network further facilitates the detection of bird's nests in an efficient and accurate manner. Extensive experiments demonstrate that the proposed approach is superior to existing methods in terms of performance and has a great deal of potential for detecting bird's nests.

Langue: Anglais