Lightweight Temporal Consistency for Grid-Based Obstacle Detection in Edge Devices

Abstract

We present a lightweight obstacle detection framework designed for deployment on resource-constrained edge devices, ranging from desktop GPUs to Raspberry Pi and microcontrollers. Instead of relying on conventional object detection outputs, our method converts images into a compact 6 × 8 binary obstacle matrix that enables efficient spatial reasoning for autonomous systems. To improve robustness against motion blur, occlusion, and lighting variations, we introduce a lightweight temporal smoothing mechanism that significantly enhances detection consistency while adding virtually no computational overhead. The framework is evaluated using YOLOv8n, SSD-MobileNet v2, and FOMO across multiple hardware platforms, demonstrating up to a 13.38 percentage point improvement in detection accuracy, while matrix projection and smoothing together require less than 0.1 ms of additional processing time. The proposed approach provides an efficient perception module for edge AI, TinyML, autonomous robots, and micro-drones.