Intelligent Home Scene Recognition Based on Image Processing and Internet of Things
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Intelligent home systems interconnect various devices within the home using Internet of Things (IoT) technology.In order to achieve the objectives of remote control, automated management, and intelligent services, these systems require robust scene recognition capabilities.However, the accuracy and real-time performance of current image processing algorithms in complex environments and diverse scenarios remain to be improved.Additionally, the interoperability and security issues among intelligent home devices are challenging to address.Therefore, this study delves into the scene recognition technology of intelligent homes based on image processing and IoT.A GLN network is constructed to process multi-view images of intelligent home scenes, enabling the determination of subregion positions within the scenes.A model aggregation algorithm based on distributed learning is proposed, selecting intelligent home edge devices as the intelligent nodes of the IoT.By processing data and training models on these intelligent nodes, distributed intelligent home scene recognition is achieved.A dual-channel deep neural network-based intelligent home scene recognition model is constructed, and experimental results verify the effectiveness of the proposed model.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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