Self-learning visual inspection system for cable crimping machines

Ghidoni, Stefano; Finotto, Matteo; Menegatti, Emanuele

doi:10.1109/icra.2011.5980590

Cited by 2 publications

(1 citation statement)

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“…This work builds on the preliminary system presented in [31], which has been expanded and thoroughly tested, and is described in detail in this paper. Even though the system presented here relies on some state-of-the-art computer vision techniques, it faces a number of issues that are peculiar to cable crimping visual inspection, like an accurate color measurement in presence of strong noise factors, and the capability of dealing with bent and overlapped wires.…”

Section: Related Workmentioning

confidence: 99%

Automatic Color Inspection for Colored Wires in Electric Cables

Ghidoni

Finotto

Menegatti

2015

IEEE Trans. Automat. Sci. Eng.

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In this paper, an automatic optical inspection system for checking the sequence of colored wires in electric cable is presented. The system is able to inspect cables with flat connectors differing in the type and number of wires. This variability is managed in an automatic way by means of a self-learning subsystem and does not require manual input from the operator or loading new data to the machine. The system is coupled to a connector crimping machine and once the model of a correct cable is learned, it can automatically inspect each cable assembled by the machine. The main contributions of this paper are: (i) the self-learning system; (ii) a robust segmentation algorithm for extracting wires from images even if they are strongly bent and partially overlapped; (iii) a color recognition algorithm able to cope with highlights and different finishing of the wire insulation. We report the system evaluation over a period of several months during the actual production of large batches of different cables; tests demonstrated a high level of accuracy and the absence of false negatives, which is a key point in order to guarantee defect-free productions

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Section: Related Workmentioning

confidence: 99%