The MS COCO (Microsoft Common Objects in Context) dataset is large-scale object detection, segmentation, key-point detection, and captioning. It is widely used for various models. We are trying to convert the deep PCB Manufacturing defect into COCO Format....
For this project, we use the public HRIPCB dataset to train, test and validate the object detection model29. The dataset contains 1386 labeled images with six different families of manufacturing defects (missing hole, mouse bite, open circuit, short, spur, spurious copper). It is based on 10...
In order to test this method, we also report a defect dataset called AM Defect Recognition Benchmark Dataset, we proved the method can achieve the state-of-art defect detection performance in actual scene dataset. 展开 关键词: Training Data models Feature extraction Image segmentation Image ...
Metal SLM parts usually produce a high reflection phenomenon. When the defect detection system based on reflective illumination performs detection on the surface of a metal part with high reflectivity, the pixels of the image sensor are often overexposed due to the strong reflected light, resulting ...
conda env create -f environment.yml -n defect-detection conda activate defect-detectionDatasetThe model is trained using the GDXray dataset. We added segmentation masks to the original dataset using a semi-automated process. The first set of masks were generated automatically assuming that the ...
It elucidates the advantages of using ML over traditional methods in each phase, starting from the pre-processing phase of design for additive manufacturing (DfAM) and parameter optimization, through the processing phase of defect detection, to the post-processing phase of part quality assessment. ...
One key difference between the image-based monitoring and defect detection of DED-LB/M and PBF-LB/M processes is the accessibility of the surfaces. During PBF-LB/M, only the top layer is visible at any one time, which limits the information accessible by the camera. Thermal imaging and di...
(fracture, pitting, glue, and wear); A 3D gear dataset was constructed with 10000 instances following the aforementioned classification. (2) Gear-PCNet+ + introduces a novel Combinational Convolution Block, proposed based on the gear dataset for gear defect detection to effectively extract the...
can be continuously increased following the diversification of defect types. Therefore, we propose an effective framework for detecting mixed-type patterns in which a simple single model, called the single shot detector, is employed. By applying the proposed model to the WM-811K dataset, we show...
The methods and parameters used for collecting these multiple datasets, and reconstructed data for each dataset‘s selected volume of interest are provided. Raw projection data from each computed tomography scan are also offered. Unanticipated artifacts within the serial sectioning experiment are ...