PENDETEKSIAN PLAT NOMOR KENDARAAN MENGGUNAKAN ALGORITMA YOU ONLY LOOK ONCE V3 DAN TESSERACT
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
Perkembangan teknologi saat ini sangat berkembang pesat. Teknologi yang saat ini sedang dilakukan pengembangan secara besar-besaran yaitu Artificial Intelligence. Artificial Intelligence atau AI memiliki berbagai macam fungsi dan tujuan tergantung dari sistem yang akan dibuat. Salah satunya yaitu pendekteksian objek dan teks dari gambar atau video. Contoh dari pemanfaatan teknologi ini yaitu pada pendeteksian objek dan teks pada plat nomor kendaraan. Pada penelitian ini dilakukan perancangan sistem dengan menggunakan algoritma You Only Look Once V3 sebagai algoritma pendeteksi objek dan Tesseract Optical Character Recognition sebagai pendeteksi teks dalam gambar. Perancangan ini akan dibantu dengan library OpenCV pada bahasa pemrogramanan python dan menggunakan dataset gambar yang sudah tersedia. Penelitian ini bertujuan untuk mengetahui tingkat keakurasian algoritma You Only Look Once V3 yang dikombinasikan dengan Tesseract Optical Character Recognition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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