Lightweight and Real-Time Object Detection on Edge Devices: A Unified Framework for Resource-Constrained Environments

Advances in edge computing have heightened the demand for object detection models capable of running efficiently on devices with constrained computational resources. This paper presents a robust hybrid detection framework that integrates template matching with Faster R-CNN to enhance detection accuracy in challenging conditions, such as occlusion, low lighting, and motion blur, while maintaining reasonable computational efficiency. Unlike conventional cloud-based detection, our approach reduces latency and improves data privacy. On the LASIESTA dataset, the proposed method achieves a mean Average Precision (mAP) of 88.2% at IoU 0.5 and 74.6% at IoU 0.75, outperforming Faster R-CNN by 4.3% in precision and 3.6% in recall. Although inference time increases modestly by 6 ms/frame compared to Faster R-CNN alone, the hybrid method consistently delivers superior robustness. While our implementation focuses on performance evaluation on a workstation, the framework design can be adapted for deployment on heterogeneous devices through additional optimization steps such as pruning and quantization. These findings demonstrate that combining classical localization techniques with deep learning models yields a practical and effective solution for real-time detection in resource-constrained environments.