MMLF: Multi-modal Multi-class Late Fusion for Object Detection with Uncertainty Estimation

TL;DR

This paper presents a novel multi-modal late fusion approach for object detection in autonomous driving, integrating multiple data sources and uncertainty estimation to improve accuracy and reliability.

Contribution

The paper introduces a pioneering multi-modal multi-class late fusion method that enhances object detection performance and incorporates uncertainty analysis for transparency.

Findings

Significant performance improvements on KITTI datasets.

Effective multi-class detection with late fusion.

Enhanced model transparency through uncertainty estimation.

Abstract

Autonomous driving necessitates advanced object detection techniques that integrate information from multiple modalities to overcome the limitations associated with single-modal approaches. The challenges of aligning diverse data in early fusion and the complexities, along with overfitting issues introduced by deep fusion, underscore the efficacy of late fusion at the decision level. Late fusion ensures seamless integration without altering the original detector's network structure. This paper introduces a pioneering Multi-modal Multi-class Late Fusion method, designed for late fusion to enable multi-class detection. Fusion experiments conducted on the KITTI validation and official test datasets illustrate substantial performance improvements, presenting our model as a versatile solution for multi-modal object detection in autonomous driving. Moreover, our approach incorporates uncertainty analysis into the classification fusion process, rendering our model more transparent and trustworthy and providing more reliable insights into category predictions.