MS-COCO + Automotive Gen1 Datasets

Spiking neural networks (SNNs) are emerging as a promising evolution in neural network paradigms, offering an alternative to conventional convolutional neural networks (CNNs). One of the most effective methods for SNN development is the CNN-to-SNN conversion process. However, existing conversion techniques are hindered by long temporal durations, or inference latencies, which negatively impact the accuracy of the converted networks. Additionally, the application of SNNs in object detection tasks remains largely under-explored. In this study, we propose a novel approach utilizing a bistable integrate-and-fire (BIF) neuron model integrated with a single-shot multi-box detector (SSD) as the detection head. Leveraging the proposed BIF neuron framework, we convert the widely used ResNet architecture into an SNN. We validate the effectiveness of our approach through object detection tasks on the MS-COCO and Automotive GEN1 datasets. Experimental results show that our conversion technique facilitates object detection with reduced temporal steps and significant enhancements in mean average precision (@mAP), achieving mAP@0.5 scores of 0.476 and 0.591 for the MS-COCO and Automotive GEN1 datasets, respectively. This research marks the first application of BIF neurons to object detection, presenting a novel advancement in the field.