A Comprehensive Study of the Effect of Spatial Resolution and Color of Digital Images on Vehicle Classification
Bibliographic record
Abstract
Vehicle-type classification is considered a core module for many intelligent transportation applications, such as speed monitoring, smart parking systems, and traffic analysis. In this paper, many vision-based classification techniques were presented relying only on a digital camera without the need for any extra hardware components. Dimension and color are two important characteristics of any digital image that affect the cost of the digital camera used in the image acquisition. In this paper, we present a comprehensive study of the effect of these two characteristics on the vehicle classification process in terms of accuracy and performance. We apply a set of different state-of-the-art image classifiers to the BIT-Vehicle and LabelMe data sets. Each data set is downscaled into different scales to generate a variety of spatial resolutions of each data set. Besides, we examine the effect of color by converting each color version to a gray-scale one. At last, we draw a valid conclusion in regards to the impact of these two characteristics (i.e., dimension and color) on the classification accuracy and performance of the image classification methods using more than 46 000 individual experiments. Experimental results show that there is no significant influence of both color and spatial resolutions of the vehicle images on the classification results obtained by most state-of-the-art image classification methods. However, there is a correlation between the spatial resolution and the processing time required by most image classification methods. Our findings can play an important role in saving not only money, but also time for vehicle-type classification systems.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".