The Line Pressure Detection for Autonomous Vehicles Based on Deep Learning
Bibliographic record
Abstract
Nowadays, vehicle line pressure detection is an important function of an intelligent transportation system. At present, the line pressure detection algorithms mainly include algorithms based on traditional features and models and algorithms based on deep learning. However, these algorithms also have shortcomings such as low detection accuracy or relying on specific scenarios. In this regard, this paper proposes a fast and accurate vehicle line detection algorithm based on deep learning for vehicle images. The algorithm builds a GooleNet-based FCN semantic segmentation network and adds a BN layer, 1 × 1 convolution, and FPN structure to improve the segmentation effect of the GooleNet-FCN network and reduce network parameters. The MobileNet-SSD (no pretrained model) network structure is used for vehicle detection. According to the relationship between the receptive field and the anchor, and then combined with specific data, the prediction branch of the network and the Default Box on the branch are modified and the FPN structure is added for feature fusion to form the final improved MobileNet-SSD network. The experimental results show that the algorithm takes an average time of 67.8 ms per frame, the detection rate of line pressing for a vehicle is 96.6%, and the deep learning models are 25.5 M and 19.2 M, respectively. The experimental results verify the effectiveness and practicality of the detection algorithm proposed in this paper.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".