Enhancing 3D Object Detection by Using Neural Network with Self-adaptive Thresholding

TL;DR

This paper presents a neural network-based self-adaptive thresholding method that dynamically adjusts detection thresholds in 3D object detection, reducing false positives and negatives in complex urban environments.

Contribution

It introduces a novel post-processing algorithm with a self-adaptive thresholding mechanism that improves detection accuracy in real-world urban scenarios.

Findings

Reduces false positives in urban environments

Enhances detection performance in adverse weather conditions

Sets a new benchmark for adaptive thresholding in field robotics

Abstract

Robust 3D object detection remains a pivotal concern in the domain of autonomous field robotics. Despite notable enhancements in detection accuracy across standard datasets, real-world urban environments, characterized by their unstructured and dynamic nature, frequently precipitate an elevated incidence of false positives, thereby undermining the reliability of existing detection paradigms. In this context, our study introduces an advanced post-processing algorithm that modulates detection thresholds dynamically relative to the distance from the ego object. Traditional perception systems typically utilize a uniform threshold, which often leads to decreased efficacy in detecting distant objects. In contrast, our proposed methodology employs a Neural Network with a self-adaptive thresholding mechanism that significantly attenuates false negatives while concurrently diminishing false positives, particularly in complex urban settings. Empirical results substantiate that our algorithm not only augments the performance of 3D object detection models in diverse urban and adverse weather scenarios but also establishes a new benchmark for adaptive thresholding techniques in field robotics.