AUTOMATED DETECTION, LOCALIZATION, AND IDENTIFICATION OF SIGNALIZED TARGETS AND THEIR IMPACT ON DIGITAL CAMERA CALIBRATION
Bibliographic record
Abstract
The increased resolution and reduced cost of commercially-available digital cameras have led to their use in close range and low-altitude airborne photogrammetric operations. In addition, the widespread adoption of Mobile Mapping and Unmanned Aerial Vehicle systems in various applications increased the demand for 3D reconstruction using Medium-Format Digital Cameras (MFDCs). The interest of professionals who might lack photogrammetric expertise mandates the development of automated procedures, especially camera calibration, for the manipulation of digital imaging systems. This paper deals with an investigation of the type of signalized targets that can be economically prepared while lending themselves to reliable detection and precise localization in the captured imagery. More specifically, checkerboard and circular targets are evaluated. Efficient techniques are introduced for their automated detection and localization as well as semi-automated identification. The impact of the proposed approaches on the quality of derived IOPs is quantified using similarity measures, which evaluate the degree of similarity of the reconstructed bundles from these IOPs. The experimental results show that automated localization of checkerboard and circular targets yield consistent IOPs. However, the detection and localization of checkerboard targets are easier and more robust to the quality of the involved imagery. Therefore, checkerboard targets are recommended as the target of choice for digital camera calibration.
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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".