Comparative Analysis of Machine Learning Models for Vision-Based Line Following Rovers
Bibliographic record
Abstract
In recent years, machine learning (ML) has advanced autonomous systems, allowing for more dynamic methods of implementing line following robots using convolutional neural networks (CNNs) to process visual data. This paper presents a line-following rover for autonomous navigation, leveraging machine learning to interpret visual input. The proposed design investigates several ML models for line classification, using a camera for visual input and a Proportional-Integral-Derivative (PID) controller for motor control. The design uses a Raspberry Pi 4B and the Sphero RVR. Five different models—DenseNet121, EfficientNet-B0, EfficientNet-B1, MobileNet, and SqueezeNet—were assessed and compared for their effectiveness in line classification and cycle computation time. The dataset was constructed by manually guiding the rover along a track generating a total of 4,378 images. Experimental findings showed that lightweight models like SqueezeNet performed better compared to larger models such as DenseNet121, with cycle computation times of 201.87ms and 271.73ms, respectively.
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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 | 0.001 |
| 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".