Real Time Fabric Defect Detection System on an Embedded DSP Platform
In industrial fabric productions, automated real time systems are needed to find out the minor defects. It will save the cost by not transporting defected products and also would help in making compmay image of quality fabrics by sending out only undefected products. A real time fabric defect detection system (FDDS), implementd on an embedded DSP platform is presented here. Textural features of fabric image are extracted based on gray level co-occurrence matrix (GLCM). A sliding window technique is used for defect detection where window moves over the whole image computing a textural energy from the GLCM of the fabric image. The energy values are compared to a reference and the deviations beyond a threshold are reported as defects and also visually represented by a window. The implementation is carried out on a TI TMS320DM642 platform and programmed using code composer studio software. The real time output of this implementation was shown on a monitor.
Code (0)
등록된 구현이 없습니다.
Tasks
Defect DetectionSimilar Papers 제목 키워드 기반
Automatic Defect Detection of Print Fabric Using Convolutional Neural Network
Automatic defect detection is a challenging task because of the variability in texture and type of fabric defects. An effective defect detection system enables manufacturers to improve the quality of processes and produc…
Defect DetectionAutomated Fabric Defect Inspection: A Survey of Classifiers
Quality control at each stage of production in textile industry has become a key factor to retaining the existence in the highly competitive global market. Problems of manual fabric defect inspection are lack of accuracy…
Defect DetectionGeneral ClassificationSurveyTexture Defect Detection in Gradient Space
In this paper, we propose a machine vision algorithm for automatically detecting defects in patterned textures with the help of gradient space and its energy. Experiments on real fabric images with defects show that the …
Defect DetectionDifferentiable NMS via Sinkhorn Matching for End-to-End Fabric Defect Detection
Fabric defect detection confronts two fundamental challenges. First, conventional non-maximum suppression disrupts gradient flow, which hinders genuine end-to-end learning. Second, acquiring pixel-level annotations at in…
Defect Detectionobject-detectionObject DetectionFab-ME: A Vision State-Space and Attention-Enhanced Framework for Fabric Defect Detection
Effective defect detection is critical for ensuring the quality, functionality, and economic value of textile products. However, existing methods face challenges in achieving high accuracy, real-time performance, and eff…
Defect Detection