hahahdamowang / yoyof-in-defect-detection Goto Github PK

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The first to adopt the YOLOF model on a metal defect NEU-DET dataset. Using several image preprocessing methods, including Edge Detection, Denoising, Sharpening, and SR, were experimented on the metal defect NEU-DET dataset. In the proposed optimized batch size, backbone, soft efficient non-maximum sup-pression (Soft-NMS), and threshold of YOLO.

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yoyof-in-defect-detection's Introduction

YOYOF-in-defect-detection

In summary, the contributions of our study are listed below.

• The first to adopt the YOLOF model on a metal defect NEU-DET dataset.

• Using several image preprocessing methods, including Edge Detection, Denoising, Sharpening, and SR, were experimented on the metal defect NEU-DET dataset.

• In the proposed optimized batch size, backbone, soft efficient non-maximum sup-pression (Soft-NMS), and IoU threshold of YOLOF; we call it the YOLOF+ model, and it shows some improvement.

• Experiments Super Resolution - BSRGAN for patches and Sharpening for rolled in scale have better results than the YOLOF+ model for patches and rolled in scale.

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hahahdamowang / yoyof-in-defect-detection Goto Github PK