(Politeknik Manufaktur Bandung, Indonesia)(2) Abdur Rohman Harits Martawireja
(Politeknik Manufaktur Bandung, Indonesia)(3) Rangga Rizky Radityo
(Politeknik Manufaktur Bandung, Indonesia)(4) Sarosa Castrena Abadi
(Politeknik Manufaktur Bandung, Indonesia)(5) Abyanuddin Salam
(Swinburne University of Technology, Australia)(6) Rizqi Aji Pratama
(Politeknik Manufaktur Bandung, Indonesia)*corresponding author
AbstractThis paper presents a two–stage machine vision and deep learning–based object classification system capable of recognizing 27 combinatorial classes (shape, color, and size) in real time on the Smart Edu Station platform. Unlike end-to-end object detection frameworks such as YOLO that jointly localize and classify objects in a single pass, this system explicitly decouples the two tasks. The first stage employs HSV (Hue Saturation Value) –based morphological segmentation to localize the Region of Interest (ROI), whereas the second stage delegates feature extraction to a custom Convolutional Neural Network (CNN) architecture built entirely from scratch. The model achieved a validation accuracy of 95.00% with a macro F1-score of 95.00% on 649 validation images, and a test accuracy of 93.00% with a macro F1-score of 92.00% on 355 independent test images. To address temporal instability, the system adopts a Consecutive Stable Detection Window (CSDW) mechanism: a label is declared valid only when the same class is detected consistently for a full 3–second window without interruption; any class change resets the counter to zero. Validated labels are transmitted via TCP/IP encapsulated in JSON format. Under controlled laboratory conditions, testing across three sessions (82 objects in total) yielded a 100% transmission success rate with zero packet loss over an isolated LAN connection.
KeywordsMachine Vision, Convolutional Neural Network, Object Detection, Object Classification, Smart Edu Station
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DOIhttps://doi.org/10.26555/ijain.v12i4.2539 |
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