The growing demand for engineers with practical competencies in industrial automation requires educational approaches that effectively integrate theoretical knowledge with hands-on experience. This study proposes and evaluates a hybrid virtual–physical learning framework for programmable logic controller (PLC) education by combining Factory I/O simulation with deployment on a physical PLC laboratory system. The proposed framework was evaluated using a quasi-experimental pre-test/post-test design with control and experimental groups involving undergraduate engineering students assigned to control and experimental groups. Educational effectiveness was assessed using five complementary performance indicators: knowledge gain, programming errors, task completion time, deployment success rate, and student confidence. The results indicated that students using the proposed framework achieved higher knowledge gains, committed fewer programming errors, completed programming tasks more efficiently, and reported greater confidence than students following a conventional laboratory approach. The findings indicate that integrating virtual industrial simulation with physical PLC implementation may support competency development and students’ preparation for real industrial automation tasks. The proposed framework provides a practical methodology for competency-based PLC education and may contribute to the modernization of engineering curricula by strengthening the connection between virtual simulation and real-world industrial practice.

In laboratory and low-cost industrial settings, automated detection and counting of small assembly components continue to be difficult tasks, especially when illumination conditions are unpredictable and annotated data is few. In this paper, a hybrid computer vision system that combines a deep learning-based object detector with traditional colour analysis is presented for the real-time identification and color-based classification of assembly cubes. The suggested method uses an HSV-based post-processing technique for colour recognition and a YOLOv8 model trained as a single-class detector for cube-shaped object localisation. While retaining good detection and classification accuracy, this division greatly lowers dataset complexity and annotation effort. A GoPro Hero 8 camera running in webcam mode at 1080p resolution and 30 frames per second was used to experimentally evaluate the system. To increase resilience against changes in illumination, object orientation, and background, a new dataset of over 400 photos was employed for both training and validation. With mAP50 values ranging from 0.93 to 0.96, experimental results show good detection ability and dependable colour categorisation in a variety of test conditions. On consumer-grade hardware, real-time operation was attained at 15–25 frames per second. The results confirm that the proposed YOLO–HSV hybrid architecture offers an efficient, interpretable, and scalable solution for color-based object detection and counting. The system is particularly suitable for educational laboratories, rapid prototyping, and low-cost assembly monitoring applications, bridging the gap between deep learning research and practical deployment.