Automatisering av arbetsprocesser med hjälp av objektdetektering iritningar för elinstallationer : Patch-baserad träning och objektdetektering

Sammanfattning

This thesis investigates how artificial intelligence and computer vision can be utilized to automate the identification of installation symbols in digital electrical drawings for the company mbiz AB. In the electrical installation industry, planning and selfmonitoring are currently performed largely manually, which is both time-consuming and labor-intensive. A key technical challenge is that the symbols are extremely small relative to the overall size and high resolution of the drawings, complicating automated analysis. The study evaluates a methodology based on the object detection model YOLOv11 in combination with SAHI (Slicing Aided Hyper Inference), a technique that divides the drawing into smaller segments to preserve detail. The results show that the model achieved a Mean Average Precision (mAP) of 0.97, confirming that patch-based inference is an effective method for handling technical documents. The study resulted in a functional prototype that integrates visual detection with automated reading of drawing numbers and coordinate translation to PDF format. The conclusion is that the technology has significant potential to increase efficiency and quality assurance in future installation projects by reducing manual tasks.