Bidirectional dynamic threshold SNN for enhanced object detection with rich spike information.

Spiking Neural Networks (SNNs), inspired by neuroscience principles, have gained attention for their energy efficiency. However, directly trained SNNs lag behind Artificial Neural Networks (ANNs) in accuracy for complex tasks like object detection due to the limited information capacity of binary spike feature maps. To address this, we propose BD-SNN, a new directly trained SNN equipped with Bidirectional Dynamic Threshold neurons (BD-LIF). BD-LIF neurons emit +1 and -1 spikes and dynamically adjust their thresholds, enhancing the network's information encoding capacity and activation efficiency. Our BD-SNN incorporates two new all-spike residual blocks, BD-Block1 and BD-Block2, for efficient information extraction and multi-scale feature fusion, respectively. Experiments on the COCO and Gen1 datasets demonstrate that BD-SNN improves accuracy by 3.1% and 2.8% compared to the state-of-the-art EMS-YOLO method, respectively, validating BD-SNN's superior performance across diverse input scenarios. Project will be available at https://github.com/Ganpei576/BD-SNN.