Conceitos Básicos
MOD-CL introduces a framework utilizing constrained loss in multi-label object detection to improve output quality.
Resumo
Standalone Note:
Introduction
Object detection is crucial for autonomous driving, requiring precise identification and action understanding.
MOD-CL enhances YOLOv8 with constrained losses for improved performance in Task 1 and Task 2.
YOLOv8 for Multi-labeled Object Detection
Modified YOLOv8 supports multiple labels per bounding box using one-hot vector encodings.
Focus on agent-wise NMS and bounding box thresholding to meet requirements efficiently.
Task 1
Semi-supervised training with Corrector and Blender models improves performance.
Utilizes constrained loss based on ROAD-R paper for enhanced learning.
Task 2
Full dataset training with constrained loss leads to outputs satisfying requirements.
MaxHS solver ensures labels meet given constraints effectively.
Conclusion
MOD-CL demonstrates the effectiveness of constrained losses in multi-label object detection tasks.
Positive impact on model performance observed in both Task 1 and Task 2 scenarios.