Jin Miao, Wang Xiaohong, Guo Ce, Yang Shufan
School of Artificial Intelligence and Software, LiaoNing Petrochemical University, Fushun, Liaoning, China.
Department of Neurosurgery, The Second Hospital of Shandong University, Jinan, Shandong, China.
Sci Rep. 2025 Apr 21;15(1):13725. doi: 10.1038/s41598-025-97913-4.
In response to the increasing demands for improved model performance and reduced energy consumption in object detection tasks relevant to autonomous driving, this research presents an advanced YOLO model, designated as ECSLIF-YOLO, which is based on the Leaky Integrate-and-Fire with Extracellular Space (ECS-LIF) framework. The primary aim of this model is to tackle the issues associated with the high energy consumption of traditional artificial neural networks (ANNs) and the suboptimal performance of existing spiking neural networks (SNNs). Empirical findings demonstrate that ECSLIF-YOLO achieves a peak mean Average Precision (mAP) of 0.917 on the BDD100K and KITTI datasets, thereby aligning with the accuracy levels of conventional ANNs while exceeding the performance of current direct-training SNN approaches without incurring additional energy costs. These findings suggest that ECSLIF-YOLO is particularly well-suited to assist the development of efficient and reliable systems for autonomous driving.
针对自动驾驶相关目标检测任务中对提高模型性能和降低能耗的需求不断增加的情况，本研究提出了一种先进的YOLO模型，称为ECSLIF - YOLO，它基于具有细胞外空间的泄漏积分发放（ECS - LIF）框架。该模型的主要目的是解决传统人工神经网络（ANN）高能耗以及现有脉冲神经网络（SNN）性能欠佳的相关问题。实证结果表明，ECSLIF - YOLO在BDD100K和KITTI数据集上实现了0.917的峰值平均精度均值（mAP），从而在不产生额外能源成本的情况下，与传统ANN的精度水平相当，同时超过了当前直接训练的SNN方法的性能。这些结果表明，ECSLIF - YOLO特别适合协助开发高效可靠的自动驾驶系统。
