Аннотация
This research investigates deep learning–based object detection architectures for food-scene recognition using the Central Asian Food Scene Dataset (CAFSD). We compare a one-stage detector (YOLOv8) and a two-stage detector (Faster RCNN) using standard object detection metrics, including mAP@50, precision, recall, and F1-score. CAFSD contains 239 food categories with 17,046 training images, 2,084 validation images, and 2,176 test images, representing a challenging benchmark due to high intra-class variation and inter-class similarity. Using transfer learning from COCO-pretrained models, YOLOv8 achieved mAP@50 ≈ 0.68, precision ≈ 0.72, and recall ≈ 0.65, while Faster R-CNN achieved mAP@50 ≈ 0.74 and recall ≈ 0.69 under the same evaluation setting. The results highlight the classic speed–accuracy trade-off: YOLOv8 provides efficient inference suitable for real-time applications, whereas Faster R-CNN offers higher detection accuracy at the cost of increased computational complexity. Overall, this study expands food-scene recognition research for under-represented cuisines and provides reproducible baseline results on CAFSD.