An Ultralightweight Object Detection Network for Empty-Dish Recycling Robots
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Bibliographic record
Abstract
The emergence of empty-dish recycling robots has alleviated problems, such as labor shortages, caused by an aging population. The detection and grasping of dishes play a crucial role in empty-dish recycling robots. However, due to the limited resources of edge devices, traditional object detection models require more space to store parameters and much computational overhead, limiting the development of empty-dish recycling robots. Therefore, this article proposes an ultralightweight dish detection model YOLO-GS for an empty-dish recycling robot. We use the modified CSPDarknet as the backbone structure and design an ultralightweight neck structure for efficient feature fusion. Meanwhile, we design a lightweight head structure for object classification and bounding box coordinate regression by combining ghost shuffle convolution (GSConv2D) and the anchor-free method. For the empty-dish recycling robot to grasp the dishes, we design a dish grasp point extraction algorithm using image processing. Finally, TensorRT is used to optimize and accelerate the model for efficient and intelligent detection of dishes on the NVIDIA Jetson Xavier NX. The experimental results show that YOLO-GS achieves 99.380% mean average precision (mAP) with a parameter amount of 0.606 M. The inference speed of the TensorRT-optimized YOLO-GS algorithm reaches 31.371 FPS, which meets the needs of real-time dish detection by the empty-dish recycling robot. The image of the empty-dish recycling robot demo is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.youtube.com/watch?v=pCBo1nzm3qU&t=22s</uri> .
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it