|The Optical Time Domain Reflectometer (OTDR) is an optoelectronic instrument used to characterize an optical fiber using the measure of scattered or reflected light from points along the fiber. The resulting signal, namely the OTDR trace, is commonly used to identify and localize possible critical events in the fiber. In this work we address the problem of automatically detecting optical events in OTDR traces, and present the first 1D object-detection neural network for optical trace analysis. Our approach takes inspiration from a successful object detection network in images, the Faster R-CNN, which we adapt to time series domain. The proposed network can both classify and localize many optical events along an input trace. Our results show that the proposed solution is more accurate than existing software currently analyzing OTDR traces, improving the mean average precision score by 27.43%. In contrast with existing solutions that are not able to distinguish many types of events, our algorithm can be trained in an end-to-end manner to detect potentially any type of optic event. Moreover, our network has been deployed on embedded OTDR devices to be executed in real-time.|
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