yu liu, jie shen, ruifan ye, shu wang, jia ren, Haipeng Pan
{"title":"FP-Deeplab:织物缺陷检测的分割模型","authors":"yu liu, jie shen, ruifan ye, shu wang, jia ren, Haipeng Pan","doi":"10.1088/1361-6501/ad5f50","DOIUrl":null,"url":null,"abstract":"\n In the pursuit of fabric production efficiency and quality, the application of deep learning for defect detection has become prevalent. Nevertheless, fabric defect detection faces challenges such as low recognition ratio, suboptimal classification performance, detection inefficiency, and high model complexity. To address these issues, an end-to-end semantic segmentation network is proposed employing an efficient encoder-decoder structure, denoted as Feature Pyramid-Deeplab (FP-Deeplab). The improvements involves enhancing the backbone network by improving the mobilenetv3 network for superior performance, a novel Atrous Spatial Pyramid Pooling with Dilated Strip Pooling (ASPP-DSP) module which combines strip pooling, dilated convolution and ASPP, to ensure an expanded receptive field and the capability to gather distant contextual information. Additionally, a Feature Pyramid module (FP module) is proposed to integrate multiscale features at various stages more efficiently. The incorporating of depth-wise separable convolution in FP-Deeplab enables significant parameter and computational cost reduction, catering to online detection requirements. Experimental results showcase the superiority of FP-Deeplab over classical and recent segmentation models. Comparative analysis demonstrates higher segmentation accuracy and reduced parameter quantity. Specifically, compared to the benchmark Deeplabv3+ model with MobileV2 as the backbone, FP-Deeplab achieves a notable increase in segmentation accuracy (F1 score and MIoU) by 4.26% and 5.81%, respectively. Moreover, the model parameters (params) are only one-fifth of the original model, indicating the efficiency and effectiveness of our proposed approach.","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":" 12","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"FP-Deeplab: A Segmentation Model for Fabric Defect Detection\",\"authors\":\"yu liu, jie shen, ruifan ye, shu wang, jia ren, Haipeng Pan\",\"doi\":\"10.1088/1361-6501/ad5f50\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n In the pursuit of fabric production efficiency and quality, the application of deep learning for defect detection has become prevalent. Nevertheless, fabric defect detection faces challenges such as low recognition ratio, suboptimal classification performance, detection inefficiency, and high model complexity. To address these issues, an end-to-end semantic segmentation network is proposed employing an efficient encoder-decoder structure, denoted as Feature Pyramid-Deeplab (FP-Deeplab). The improvements involves enhancing the backbone network by improving the mobilenetv3 network for superior performance, a novel Atrous Spatial Pyramid Pooling with Dilated Strip Pooling (ASPP-DSP) module which combines strip pooling, dilated convolution and ASPP, to ensure an expanded receptive field and the capability to gather distant contextual information. Additionally, a Feature Pyramid module (FP module) is proposed to integrate multiscale features at various stages more efficiently. The incorporating of depth-wise separable convolution in FP-Deeplab enables significant parameter and computational cost reduction, catering to online detection requirements. Experimental results showcase the superiority of FP-Deeplab over classical and recent segmentation models. Comparative analysis demonstrates higher segmentation accuracy and reduced parameter quantity. Specifically, compared to the benchmark Deeplabv3+ model with MobileV2 as the backbone, FP-Deeplab achieves a notable increase in segmentation accuracy (F1 score and MIoU) by 4.26% and 5.81%, respectively. Moreover, the model parameters (params) are only one-fifth of the original model, indicating the efficiency and effectiveness of our proposed approach.\",\"PeriodicalId\":2,\"journal\":{\"name\":\"ACS Applied Bio Materials\",\"volume\":\" 12\",\"pages\":\"\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-07-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Bio Materials\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1088/1361-6501/ad5f50\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, BIOMATERIALS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1088/1361-6501/ad5f50","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
FP-Deeplab: A Segmentation Model for Fabric Defect Detection
In the pursuit of fabric production efficiency and quality, the application of deep learning for defect detection has become prevalent. Nevertheless, fabric defect detection faces challenges such as low recognition ratio, suboptimal classification performance, detection inefficiency, and high model complexity. To address these issues, an end-to-end semantic segmentation network is proposed employing an efficient encoder-decoder structure, denoted as Feature Pyramid-Deeplab (FP-Deeplab). The improvements involves enhancing the backbone network by improving the mobilenetv3 network for superior performance, a novel Atrous Spatial Pyramid Pooling with Dilated Strip Pooling (ASPP-DSP) module which combines strip pooling, dilated convolution and ASPP, to ensure an expanded receptive field and the capability to gather distant contextual information. Additionally, a Feature Pyramid module (FP module) is proposed to integrate multiscale features at various stages more efficiently. The incorporating of depth-wise separable convolution in FP-Deeplab enables significant parameter and computational cost reduction, catering to online detection requirements. Experimental results showcase the superiority of FP-Deeplab over classical and recent segmentation models. Comparative analysis demonstrates higher segmentation accuracy and reduced parameter quantity. Specifically, compared to the benchmark Deeplabv3+ model with MobileV2 as the backbone, FP-Deeplab achieves a notable increase in segmentation accuracy (F1 score and MIoU) by 4.26% and 5.81%, respectively. Moreover, the model parameters (params) are only one-fifth of the original model, indicating the efficiency and effectiveness of our proposed approach.
期刊介绍:
ACS Applied Bio Materials is an interdisciplinary journal publishing original research covering all aspects of biomaterials and biointerfaces including and beyond the traditional biosensing, biomedical and therapeutic applications.
The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important bio applications. The journal is specifically interested in work that addresses the relationship between structure and function and assesses the stability and degradation of materials under relevant environmental and biological conditions.