Design and analysis of a hybrid spiking–deep neural network for energy-efficient object detection

Abstract

In autonomous vehicular systems, object detection performs a fundamental task in which real-time processing and energy e!ciency are crucial for execution on edge devices. Faster R-CNN, which are conventional deep neural network based detectors, provide high accuracy but at the same time sustain substantial power consumption because of continuous and computationally intensive operations. This research proposes the design of a hybrid spiking deep neural network that is energy e!cient in object detection by integrating the architecture of SNN (Spiking Neural Network) that uses LIF (Leaky Integrate and Fire) neurons and temporal spike processing into the feature extraction stage, reducing redundant neural activities simultaneously preserving e”ective object detection functionality by utilizing event-driven spiking computation. The framework is assessed on the KITTI dataset which comprises real world scenarios of autonomous driving. Experimental evaluation shows that it achieves a mAP@0.5 of 0.7395, a mAP@0.7 of 0.5926 and a COCO style mAP@[0.50:0.95] of 0.4771 along with high recall and robust localization. Moreover, the hybrid model enhances detection accuracy over regular SNN models and improves recognition of small and distant objects. These results validate that the presented model has a potential and promising aspect for constructing energy e!- cient object detection systems for autonomous and edge based deployment.