YOLOv10 with Kolmogorov-Arnold networks and vision-language foundation models for interpretable object detection and trustworthy multimodal AI in computer vision perception

1. 1Employ Kolmogorov-Arnold networks as an interpretable post-processing step for object detection
2. 2Integrate vision-language foundation models to enhance multimodal understanding
3. 3Evaluate the approach on visually degraded or ambiguous scenes to assess reliability
4. 4Fine-tune the model for improved performance on specific computer vision tasks

Computer vision engineers and researchers on autonomous vehicle projects benefit from this approach as it provides more transparent and trustworthy object detection capabilities

💡 Kolmogorov-Arnold networks can provide interpretable confidence scores for object detection in visually degraded scenes

💡 YOLOv10 + Kolmogorov-Arnold networks = more transparent object detection in computer vision