Details

Autor(en) / Beteiligte

• Guo, Shuai
• Li, Dengao
• Zhao, Jumin
• Tang, Bao
• Luo, Biao

Titel

MFSF-Net: A Multiscale Feature and Side-Outputs Fusion Network for Pixelwise Catastrophic Optical Damage Detection

Ist Teil von

• IEEE transactions on instrumentation and measurement, 2024, Vol.73, p.1-10

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2024

Quelle

IEL

Beschreibungen/Notizen

• Catastrophic optical damage (COD) is one of the crucial factors severely constraining the performance of high-power lasers. An accurate COD defect location is of great significance to laser chip manufacturing, which could be used to improve the production process and optimize the structural design of laser chips. A manual detection method for laser chips is very time-consuming and costly. Recently, deep-learning-based methods have demonstrated outstanding performance in various fields, owing to their robust feature extraction capabilities. However, these methods still have limitations on the samples with weak texture, class imbalance issues, and random size of targets. To address these issues, a novel COD defect segmentation method is proposed. And electroluminescence imaging technology is utilized to visualize defects inside the laser chip and collect the COD dataset. To improve the extraction capacity of the strip-like COD feature, a rectangular dilated convolution is proposed to increase the receptive field of the convolution. To acquire richer information from local contextual features, a multiscale feature aggregation block (MFAB) consisting of multiscale rectangular dilated convolutions is introduced to acquire multiscale feature maps. An attention module is applied in the proposed block to highlight the defect features. Moreover, to enhance the segmentation capacity on random-scale defects and class imbalance issues, a deeply supervised side-outputs fusion block is proposed to fuse multiple side outputs at different semantic levels to generate the final segmentation map, which is used to improve COD detection performance in the way of feature pyramid. Experimental results on the COD segmentation dataset demonstrate that the proposed method outperforms other state-of-the-art segmentation methods.