dlvr.it
AI, Vol. 7, Pages 305: EcoSortBin:
Accuracy–Generalisation Trade-Offs in Open-Vocabulary
Campus Waste Detection on Raspberry Pi 4
Waste management on university campuses is complicated by the constant change in packaging types, which existing waste-sorting systems cannot recognise unless they are retrained. Open-vocabulary object detectors can identify objects from text descriptions instead of a fixed list of categories, offering a possible solution to this problem. However, it is not known how well this ability survives when such a detector is fine-tuned and deployed on low-power hardware. This paper presents EcoSortBin, a waste-sorting system built on the YOLOE-26 detector and deployed on a Raspberry Pi 4. YOLOE-26 was first fine-tuned on a 1330-image campus waste dataset covering seven classes, with masks generated using the Segment Anything Model, establishing a baseline called WasteYOLOE26-S with 74% top-1 accuracy on known classes; however, this fine-tuning reduces the model’s ability to recognise the same seven classes when they appear in a different dataset or setting. RLPA (RepRTA-Compatible LoRA Prompt Adapters) addresses this by adapting only the text-embedding component of the model using a small set of additional parameters (16,384 parameters, rank 16), leaving the rest of the network unchanged; this restores cross-domain generalisation but reduces top-1 accuracy on known classes to only 15%, which is too low for practical use. To recover this accuracy without losing cross-domain generalisation, frozen-backbone neck fine-tuning (NeckFT) was added, which fine-tunes the feature-combining layers of the network while keeping the main backbone frozen, preserving its pretrained visual–text alignment. Combining RLPA with NeckFT achieved the best balance of the three approaches, with 73.5% top-1 accuracy and a Cross-Domain Generalisation Ratio (CDGR) of 0.2435. To test whether this ability extends to genuinely new categories, the model was further tested on 28 novel categories not seen during training, totalling 840 images. RLPA + NeckFT showed consistent zero-shot generalisation to novel objects with container-like shapes, such as bottles and jars. After quantisation for edge deployment, the model kept its full accuracy ranking and produced a compact 41.8 MB file suitable for the Raspberry Pi 4. These results show that RLPA + NeckFT gives a practical balance of accuracy and generalisation for campus waste detection on low-power hardware.
