BibTex Citation Data :
@article{JPA29716, author = {Ayu Sarah Annisa and Meqorry Yusfi}, title = {Classification of Box Sizes in Automated Systems Using YOLOv8 and Computer Vision}, journal = {Journal of Physics and Its Applications}, volume = {8}, number = {2}, year = {2026}, keywords = {Instrumentation and Electronics; Applied Physics}, abstract = { The logistics and distribution industry requires a fast and accurate automated sorting system to improve operational efficiency. This research develops a computer vision-based automated sorting system using YOLO (You Only Look Once) to detect and classify box sizes in real time. The system consists of an ESP32-CAM as a visual sensor, an ESP8266 NodeMCU as a microcontroller, and a servo motor as an actuator using the MQTT communication protocol. The detection results are sent through a local MQTT broker for low latency processing without the internet. The YOLOv8 model used successfully achieved a detection and classification accuracy of 98.15%. The top camera showed more stable performance (89-96%) than the front camera (83-96%) due to the influence of distance and angle of image capture. The tests were conducted under fixed lighting conditions and only distinguished between small (< 5×5×5 cm) and large (≥ 5×5×5 cm) boxes, with a maximum load limit of 700 grams. The system is still limited in classifying objects close to the size limit, and is not optimal for variable lighting. }, issn = {2622-5956}, pages = {153--160} doi = {10.14710/jpa.v8i2.29716}, url = {https://ejournal2.undip.ac.id/index.php/jpa/article/view/29716} }
Refworks Citation Data :
The logistics and distribution industry requires a fast and accurate automated sorting system to improve operational efficiency. This research develops a computer vision-based automated sorting system using YOLO (You Only Look Once) to detect and classify box sizes in real time. The system consists of an ESP32-CAM as a visual sensor, an ESP8266 NodeMCU as a microcontroller, and a servo motor as an actuator using the MQTT communication protocol. The detection results are sent through a local MQTT broker for low latency processing without the internet. The YOLOv8 model used successfully achieved a detection and classification accuracy of 98.15%. The top camera showed more stable performance (89-96%) than the front camera (83-96%) due to the influence of distance and angle of image capture. The tests were conducted under fixed lighting conditions and only distinguished between small (< 5×5×5 cm) and large (≥ 5×5×5 cm) boxes, with a maximum load limit of 700 grams. The system is still limited in classifying objects close to the size limit, and is not optimal for variable lighting.
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