MedPackID: An Indonesian Benchmark Dataset for Medicine Packaging and Pill Detection Using YOLOv8n and Faster R-CNN
Main Article Content
Qory Hidayati
Hendra Kusuma
Muhammad Attamimi
Background: Reliable image-based medicine recognition is necessary to support safer medication handling, reduce identification errors, and improve automated access to pharmaceutical information. However, its performance may degrade when medicine packaging, pills, and printed text vary across acquisition conditions.
Aims: This study aimed to develop MedPackID as a public Indonesian benchmark for medicine packaging and pill detection and to establish reproducible detection and conditional recognition baselines.
Methods: MedPackID contains 1,444 consumer-device images and 1,540 manually reviewed bounding boxes collected at one site using one smartphone under varied illumination, viewpoint, orientation, scale, background clutter, and partial occlusion. YOLOv8n and Faster R-CNN were evaluated for object detection. Detected packaging regions were processed using EasyOCR, text normalization, and fuzzy matching, whereas detected pill regions were evaluated using ResNet-18 embeddings and cosine-similarity retrieval.
Results: On the frozen test partition, YOLOv8n achieved mAP@0.5 of 0.968 and mAP@0.5:0.95 of 0.707, compared with 0.909 and 0.645 for Faster R-CNN. Packaging retrieval achieved 90.98% Top-1 accuracy, while closed-set pill retrieval achieved 85.4% Top-1 accuracy. MedPackID provides a reproducible regional benchmark for pharmaceutical object detection and conditional recognition. The results establish within-dataset reference performance rather than clinical validity or generalization to unseen medicine identities, devices, or acquisition sites.
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Qory Hidayati , Department of Electrical Engineering, Faculty of Intelligent Electrical and Informatics Technology (F-ELECTICS), Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
Department of Electrical Engineering, Balikpapan State Polytechnic, Balikpapan, Indonesia
