Object ground lines regression and mapping from fisheye images to around view image for the AVP
Bibliographic record
Abstract
The automated valet parking (AVP) based on the around view monitor (AVM) has attracted increasing attention from the research community due to its high practicality and low cost. However, the distortion correction and inverse perspective transformation of the around view images result in the truncated and deformed objects, which leads to poor detection performance. To address this problem, we propose a method of object ground lines regression and mapping from fisheye images to around view images. There are two key steps for the proposed method, regressing ground lines in the fisheye images and mapping ground lines to the around view image. To detect the objects and regress ground lines at the same time, we explored three new detectors based on YOLOv5 (i.e. YOLOv5-VGG, YOLOv5-Point, and YOLOv5-Ratio). Among them, YOLOv5-VGG adds a VGG-based regression network behind YOLOv5, YOLOv5-Point directly adds a regression head to share the convolutional features, and YOLOv5-Ratio combines the 2D box and ground line to predict its ratios. After that, the ground lines from four different views are mapped and fused to the around view image. Additionally, to facilitate the study of the object detection in the around view image, a large-scale labeled dataset is established, which comprises 9828 fisheye images collected from typical indoor and outdoor parking lots. For each image, the 2D bounding boxes and ground lines of objects are carefully labeled. Experiments show that the YOLOv5-Ratio obtains the best performance and can effectively detect the truncated and deformed object in the around view image with the precision rate of 92.28% and recall rate of 85.09% on our collected dataset.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".