Real-time laser spot detection and tracking system based on parallel multi-target detection and determination algorithm
Bibliographic record
Abstract
Laser spot detection and tracking play a critical role in laser techniques. However, traditional detection and tracking systems tend to be bulky and lack portability. Therefore, there is a growing emphasis on developing high-performance and miniaturized systems based on the field programmable gate array (FPGA). In this paper, a novel parallel multi-target detection and determination algorithm is proposed to address the issue of current FPGA-based systems' ineffective detection of laser spots in complex environments. Our simulation results demonstrate that the algorithm can effectively detect laser spots in complex environments. It can process a frame with an 800 × 480 resolution in only 7.88 ms at a 50 MHz image processing frequency, which means it can process more than 100 f/s and meet the real-time detection requirements. Such excellent detection performance is challenging to achieve with central processing units and advanced RISC machine microprocessors. Then, the algorithm is further deployed on an FPGA to build a prototype laser spot detection and tracking system. Practical tests show that the system can achieve a spot detection accuracy of around 90% under different luminous intensities, indicating excellent robustness of the designed algorithm. Besides, with the use of a piezoelectric actuator, speedy and precise tracking of the laser spot is implemented. The characteristics of speedy response, self-latching in power off, and no electromagnetic interference of the piezoelectric actuator give the system tremendous advantages in developing high-precision wireless communication control technology, which further broadens the application of the proposed system.
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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".