Detection of Metal Surface Scratch Based on YOLOv4 Network Model

doi:10.12382/bgxb.2022.A011

Abstract

Abstract: Metal surface scratch detection aims to achieve the classification recognition and precise scale localization of metal scratchs. The current traditional image processing methods are difficult to achieve the accurate localization and recognition of scratches due to their different shapes and low contrast with the background.A shallow neural network model based on small scale convolution kernel is proposed. The proposed model uses the correlation theory of small target detection to enhance the scratches at the data level first and then train the network model to achieve the accurate detection of surface scratches.Compared with the original YOLOv4 model， the proposed model can avoid the missing detection and false detection of insignificant scratches better， and also can extract the penetrating or long scratches more accurately and completely.The model can fully meet the requirements of accurate inspection of production line.

Key words: metalsurface, scratchdetection, YOLOv4networkmodel, NEUdataset

CLC Number:

TP391.4

ZHANG Boyao， LENG Yanbing. Detection of Metal Surface Scratch Based on YOLOv4 Network Model[J]. Acta Armamentarii, 2022, 43(S1): 214-221.

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表5 金属表面划痕检测结果视觉比较（优化模型提取的目标范围信息较为完整，
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