Research on the application of artificial intelligence and multi-scale image fusion technology to pedestrian detection in complex street view
Bibliographic record
Abstract
With the increasing face imaging data and the advancement of artificial intelligence (AI) technology, computer-aided monitoring systems are crucial for pedestrian detection in dense street view. However, due to occlusion and small pedestrian scale, pedestrian false alarms and missed detection problems become more and more serious. Therefore, this paper proposes a pedestrian detection model, YOLOv10s-pedestrian. Firstly, CA attention is introduced to redesign the MBConv module, resulting in an efficient MB-CANet backbone for pedestrian feature extraction, enhancing the accurate localization of densely occluded pedestrians. Secondly, a novel C2FN structure was created to reduce the number of parameters while improving the model's accuracy. Additionally, inspired by the BiFPN feature fusion concept, a Bi-C2FN-FPN network structure is proposed to effectively fuse features from different depth sources, strengthening feature fusion and improving pedestrian detection accuracy. Finally, the MPDIOU loss function replaces the original CIoU loss function to enhance anchor box localization. Experimental results demonstrate that the proposed model achieves a mAP50 of 95.6% on the WiderPerson pedestrian detection dataset, which is a 6.1% improvement over the original model, with a recall rate of 86.2%, showcasing excellent detection performance. Compared to several mainstream object detection models, the proposed model also exhibits superior performance.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".