Multi-Scale Performance Benchmarking of YOLO Models for Effervescent Tablet Defect Detection

Effervescent tablets are highly hygroscopic solid dosage forms in which even minor surface defects can compromise product stability, dose uniformity, and patient safety. Reliable, high-throughput defect detection is therefore essential, yet the existing literature overwhelmingly focuses on compressed or film-coated tablets and rarely offers a systematic comparison across recent YOLO families and scales. This study presents a multi-scale performance benchmarking of three recent YOLO families—YOLO11, YOLO12, and YOLO26—on a newly constructed effervescent tablet defect dataset. The dataset comprises 251 high-resolution images acquired under controlled illumination, each containing 12 tablets, and is manually annotated in YOLO format across six physical-condition classes (intact, damaged, cracked, broken, moist, and stained), yielding 3,012 bounding-box instances. All five standard scale variants (n, s, m, l, x) of each family were trained for 100 epochs under identical hyper-parameter settings, producing fifteen model variants that are compared in terms of mAP@0.5, mAP@0.5:0.95, precision, recall, inference speed (FPS), parameter count, and FLOPs. Experimental results show that YOLO11l achieves the best overall accuracy, with 96.8% mAP@0.5 and 91.7% mAP@0.5:0.95, while YOLO11n offers the most attractive real-time trade-off at 345.9 FPS with 95.6% mAP@0.5 and only 2.5M parameters. YOLO12 variants deliver competitive accuracy but at markedly lower inference speeds for the larger scales, whereas YOLO26 scales lag in the nano regime (88.0% mAP@0.5) but close the gap at l/x scales. Class-wise analysis of YOLO11l shows consistently high performance across all six defect categories, with mAP@0.5 ranging from 0.940 (damaged) to 0.994 (stained). The results provide practical guidance for selecting a YOLO configuration for real-time effervescent tablet inspection lines and demonstrate that modern nano- and small-scale detectors are already sufficient for high-throughput pharmaceutical quality control.