Detecting the tiny defects of cigarette appearance based on a hybrid model of lightweight ViT and RCNN

ShiChao Wu; XingXu Li; XianZhou Lv; YingBo Liu

doi:10.1117/12.2660385

3 February 2023 Detecting the tiny defects of cigarette appearance based on a hybrid model of lightweight ViT and RCNN

ShiChao Wu, XingXu Li, XianZhou Lv, YingBo Liu

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 125113C (2023) https://doi.org/10.1117/12.2660385
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

Detecting tiny defects in cigarettes is currently a major concern for manufacturers. To address this issue, this paper investigates a hybrid model based on lightweight ViT and RCNN to provide a better balance of high performance and high accuracy. Experiments showed that the model presented in this paper has a mAP value of 85.7% at 1% of tiny defects in cigarette appearance and an inference speed of 82 FPS in an acquisition scenario with a camera resolution of 1280×280, which meets the needs of high-speed acquisition in industrial sites. The results indicate that the hybrid model can be used to detect flaws in cigarette appearance.

Citation Download Citation

ShiChao Wu, XingXu Li, XianZhou Lv, and YingBo Liu "Detecting the tiny defects of cigarette appearance based on a hybrid model of lightweight ViT and RCNN", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 125113C (3 February 2023); https://doi.org/10.1117/12.2660385

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