Deep Learning-Based Application for Automatic Product Detection in a Commercial Sector Company in Trujillo

This research focuses on developing a system using deep learning techniques for the automatic detection of products at the company Negociaciones 7 E.I.R.L., with the aim of reducing errors and providing a clear, efficient alternative to traditional record-keeping. To this end, three architectural models were evaluated: YOLO11n, YOLOv8s, and YOLOv8n, taking into account metrics such as accuracy, recall, mAP, and detection time. The results show that YOLOv8s performs best, achieving an accuracy of 92%, a recall of 90%, and an mAP50 of 91%, standing out for its balance between accuracy and speed. In contrast, YOLOv8n demonstrated a rapid response, albeit with intermediate performance (85% accuracy and an mAP50 of 87%), whilst YOLO11n showed low accuracy and stability at 80%. The implementation of the system enabled a 80% reduction in registration errors and optimised operational efficiency. The process was guided by the CRISP-DM methodology, covering everything from image processing and data collection to integration with SQL Server. Furthermore, statistical validation, using non-parametric tests such as the Mann-Whitney U, Kolmogorov-Smirnov, and Welch’s t-tests, confirmed the significance of the improvements. The results demonstrate that the YOLO architecture optimizes productivity, aids inventory management, and contributes to digital transformation in the retail sector. Furthermore, in the context of Peru, particularly in Trujillo, there are few similar studies, which highlights its innovative nature.