Abstract
ABSTRACT Crack detection is one of the important contents of the nuclear fuel pellet quality inspection. Aiming at the problem of high crack false detection rate caused by low image contrast of nuclear fuel pellets, complex image background and fine cracks on the pellet surface, a Weighted Object Variance (WOV) threshold crack detection method guided by SE-CrackNet convolutional neural network is proposed. The method first uses the sliding window scanning technology and SE-CrackNet network to locate the crack regions in the pellet image, and then uses the WOV threshold method to extract the cracks to achieve accurate identification of the cracks on the surface of the nuclear fuel pellet. The pixel-level F1-measure of the method is about 92%, which can accurately identify cracks on the surface of nuclear fuel pellets, greatly reduce the crack false detection rate, meet the real-time quality inspection requirements of nuclear fuel pellet production lines, and vastly improve the performance of traditional machine vision inspection systems. At the same time, the method can be extended to the quality inspection of other industrial products.
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