Industrial 3D Anomaly Detection and Localization Using Unsupervised Machine Learning

Detecting defects in industrially manufactured products is crucial to ensure their safety and quality. This process can be both expensive and error-prone if done manually, making automated solutions desirable. There is extensive research on industrial anomaly detection in images, but recent studies have shown that adding 3D information can increase the performance. This thesis aims to extend the 2D anomaly detection framework, PaDiM, to incorporate 3D information. The proposed methods combine RGB with depth maps or point clouds and the effects of using PointNet++ and vision transformers to extract features are investigated. The methods are evaluated on the MVTec 3D-AD public dataset using the metrics image AUROC, pixel AUROC and AUPRO, and on a small dataset collected with a Time-of-Flight sensor. This thesis concludes that the addition of 3D information improves the performance of PaDiM and vision transformers achieve the best results, scoring an average image AUROC of 86.2±0.2 on MVTec 3D-AD

Industrial 3D Anomaly Detection and Localization Using Unsupervised Machine Learning

Detecting defects in industrially manufactured products is crucial to ensure their safety and quality. This process can be both expensive and error-prone if done manually, making automated solutions desirable. There is extensive research on industrial anomaly detection in images, but recent studies have shown that adding 3D information can increase the performance. This thesis aims to extend the 2D anomaly detection framework, PaDiM, to incorporate 3D information. The proposed methods combine RGB with depth maps or point clouds and the effects of using PointNet++ and vision transformers to extract features are investigated. The methods are evaluated on the MVTec 3D-AD public dataset using the metrics image AUROC, pixel AUROC and AUPRO, and on a small dataset collected with a Time-of-Flight sensor. This thesis concludes that the addition of 3D information improves the performance of PaDiM and vision transformers achieve the best results, scoring an average image AUROC of 86.2±0.2 on MVTec 3D-AD

Support for digital chat and communication in healthcare : A thesis exploring a system developed for communication between patients and healthcare workers.

Denna rapport är ett kandidatarbete vid Linköpings Universitet i kursen TDDD96. Rapporten är baserad på ett projekt som gruppen har utfört där de har byggt ett chattsystem åt en extern kund, Region Östergötland. Chattsystemet som byggdes var en chattapplikation som kan användas mellan patienter och vårdpersonal, även en chattbott användes. Chattapplikationen fokuserade på hur förgrening av konversationer kunde ske på ett smidigt och användarvänligt sätt samt att vårdpersonalen kan markera viktiga meddelanden. I projektet användes Scrum vilket är ett ramverk som bygger på utveckling som sker i sprintar, dagliga möten samt utvärdering efter varje sprint. Sist i rapporten ligger ett antal olika individuella bidrag som varje projektmedlem har gjort