Detection and Recognition of Flower Image Based on SSD network in Video Stream
Bibliographic record
Abstract
Abstract At present, most flower images could only be recognized but not detected. They can only be used in the scenes with a single target instead of the scenes with two or more targets. Some application scenarios require the human-computer interaction mode with the current location information of flowers; moreover, due to the complexity of the environment and the similarity and difference between flowers, the traditional computer visual methods are inefficient and inaccurate. Therefore, this study introduced SSD deep learning technology into the field of flower detection and identification. The flower data set published by Oxford University was used as the research object, and it was used as the input of the neural network model for training and testing. The experimental results show that the average accuracy is 83.64% based on the evaluation standard of Pascal VOC2007, and 87.4% based on the evaluation standard of Pascal VOC2012. The processing time of an image on PC is 0.13s, which indicates that high-quality automatic detection and recognition can be performed, which can facilitate the retrieval of agricultural plant information database and help people to popularize related information of flowers.
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.
How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".