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

Overview of Neuromorphic Object Detection

Neuromorphic object detection represents a shift from traditional frame-based vision to asynchronous, event-driven sensing. Unlike conventional cameras that capture images at fixed intervals, neuromorphic cameras (such as DVS, ATIS, and DAVIS) record changes in light intensity as a continuous stream of events. This paradigm offers high temporal resolution, low latency, and high dynamic range, making it particularly effective for high-speed motion and challenging lighting conditions.

Taxonomy of Detection Models

The authors categorize existing neuromorphic object detection algorithms into three primary paradigms:

• Synchronous Image-like ANNs: These models convert event streams into 2D image-like representations, allowing the use of standard frame-based architectures. While this is currently the most accurate approach, it often fails to fully exploit the spatiotemporal nature of event data.
• Asynchronous Sparse/Graph-based ANNs: These methods process events individually or as sparse graphs. They are designed for low-latency applications, though they often lag behind synchronous models in raw detection accuracy.
• Bio-inspired Spiking Neural Networks (SNNs): These networks use spike-driven dynamics to process information. They are theoretically the most energy-efficient, though they remain difficult to train for complex regression tasks like object detection.

As robotics, autonomous driving, and edge computing demand faster and more efficient visual perception, conventional cameras often become bottlenecks due to motion blur and high power consumption. This paper serves as a foundational resource for researchers to understand the current state of the field, the trade-offs between different architectural paradigms, and the remaining challenges—such as the need for better event-driven training strategies—that must be addressed to move beyond frame-based limitations.