2019/10/21 by Maria Spichkova, Johan van Zyl, Johan van Zÿl +9 · 1 citation
Computer Science · Engineering · #Advanced Neural Network Applications #Computers and Society (cs.CY) #Cryptography and Security (cs.CR) #Currency Recognition and Detection #Electricity Theft Detection Techniques #FOS: Computer and information sciences #Smart Grid Energy Management #cs.CR #cs.CY
paper · pdf · doi:10.48550/arxiv.1910.12617
Preprint. Accepted to the 14th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2019). Final version published by SCITEPRESS
openalex publication_date 2019/10/21 · arxiv created 2019/10/22 · arxiv updated 2019/10/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Electricity and gas meter reading is a time consuming task, which is done manually in most cases. There are some approaches proposing use of smart meters that report their readings automatically. However, this solution is expensive and requires (1) replacement of the existing meters, even when they are functional and new, and (2) large changes of the whole system dealing with the meter readings. This paper presents results of a project on automation of the meter reading process for the standard (non-smart) meters using computer vision techniques, focusing on the comparison of two computer vision techniques, Google Cloud Vision and AWS Rekognition.