Neuromorphic Object Detection: An In-Depth Study and Future Directions

2026-07-26

AI Summary

This work addresses the critical lack of systematic evaluation benchmarks for neuromorphic vision, which holds promise for overcoming the performance limitations of conventional frame-based cameras in high-speed motion or low-light conditions. The study presents the first comprehensive benchmark for neuromorphic object detection, encompassing key dimensions such as event representation, temporal modeling, multimodal fusion, and asynchronous processing. Through extensive comparative experiments, the authors rigorously evaluate representative algorithms under a unified framework, offering in-depth analysis of their accuracy, energy efficiency, and practical applicability. The findings not only clarify current challenges but also establish a standardized evaluation protocol and provide actionable insights to guide future research in this emerging field.

Abstract

Conventional frame-based cameras face significant challenges in detecting objects under high-speed motion blur or in low-light environments. Neuromorphic cameras provide asynchronous visual streams with high temporal resolution and a wide dynamic range, offering a promising solution for object detection under challenging conditions. Despite the development of numerous models and the emergence of various applications in neuromorphic object detection, there is still a lack of deep understanding and standardized benchmarks to assess progress and address key challenges. In this paper, we provide a comprehensive survey and benchmark of existing neuromorphic object detection algorithms. Specifically, we first present a problem description, review the available datasets, and revisit the evaluation metrics. We then explore existing neuromorphic object detection approaches from various perspectives, including event representation, temporal modeling, multimodal fusion, asynchronous processing, low-latency processing, and energy-efficient computing. Furthermore, we evaluate a wide range of representative neuromorphic object detection models and offer detailed analyses of the comparative results. Finally, we discuss unresolved issues in neuromorphic object detection and propose potential future research directions. We hope this survey and benchmark will be a valuable resource for researchers and provide guidance for future advancements in neuromorphic object detection.

Problem

• neuromorphic object detection
• motion blur
• low-light environments
• standardized benchmarks
• asynchronous visual streams

Innovation

• neuromorphic vision
• event-based object detection
• benchmarking
• asynchronous processing
• temporal modeling