Poster: Edge Computing for Deep Learning-based Sensor Multi-Target Detection

Abstract

This study purposes a real-time computing of deep learning-based multi-target detection in defense-purpose edge sensors. Our study suggests two fundamental optimizations to accelerate target detection inference model: algebraic enhancements and post-training quantization. Comprehensive benchmark results show that our computing design achieves real-time multi-target detection on energy-efficient edge devices.