Road sign detection algorithm based on improved YOLOV4
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The detection of traffic signs is an important part of the research on automatic driving. Road traffic signs occupy the edge of the image, the image is small, and the detection accuracy is low. The improved YOLOv4 target detection algorithm is used to detect road traffic signs. The original activation function is modified to the h-swish activation function. The input image is convolved by 1x1 to obtain the image feature concentration. The main feature extraction network adds depth separable convolution and residual edge parts, and introduces attention mechanism to enhance the feature extraction performance. The road sign prior frame is regenerated using K-means clustering algorithm, The clustering algorithm can achieve network convergence. After the test, it is shown that by training and evaluating the CCTSDB dataset, MAP@0.5 83.47%, 2.78% higher than the original YOLOv4; The parameter quantity of the network model is 45.60M, which is 18.5% of the size of the original YOLOv4 model. The network becomes lightweight, and the target detection of road signs can be well achieved through testing.
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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 | 0.001 | 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 | 0.004 |
| 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 it