A Survey of Hybrid ANN-SNN Structures of Event-Based Object Detection

Abstract In comparison with regular cameras event based cameras operate are quite differently. They is extremely quick, power, efficient, and dependable in a challenging situations like these low light or fast motion since they just communicate changes in the scene rather than complete images. Still its creating object detection algorithms that can be fully leverage this unique data can still be tough Such as Spiking Neural Networks (SNNs) is better suited for event streams still more difficult to train or scale Traditional Artificial Neural Networks (ANNs) achieve are excellent accuracy but struggle to handle the sparse and asynchronous nature of event data. Recent studies have concentrated on hybrid ANN–SNN models, which incorporate the advantages of both strategies, in order to avoid this As the ANN Frequently Upgrade with spatial and temporal attention its also transformer modules manage high- level feature learning also object detection are these systems SNNs effectively process raw event streams and capture fine grained temporal information. These hybrid models can be achieve competitive more higher detection accuracy while lowering latency and power consumption when its compared to purely ANN based solutions according to studies also streaming vision transformers, hybrid attention networks, and energy-aware ANN SNN pipelines. Recent hybrid ANN–SNN techniques for event-based object detection are compiled and explained in an understandable manner in this review. In this highlights sectors in hybrid models perform best and tells about typical common design decisions, training methods, datasets, and performance trends. The review finds that hybrid ANN–SNN architectures are a powerful and useful approach for developing precise, real-time, and energy-efficient event-based object identification systems. It also highlights problems are unsolved issues like fair benchmarking and fully streaming learning.