Optimization of MobileNet SSD Using Pruning, Quantization, and Transfer Learning for Real-Time Vehicle Detection in IoT-Based Security Systems - Afit Miranto; Purwono Prasetyawan; Iqbal May Aryanto (Jurnal Teknik Informatika (Jutif), 2026)
Security is a critical requirement in modern public and private environments, especially in systems that rely on resource-constrained IoT devices. This research aims to optimize the MobileNet SSD (Single Shot MultiBox Detector) model to achieve fast and reliable real-time vehicle and human detection on low-power hardware. The proposed optimization pipeline integrates three techniques: pruning to reduce network redundancy, quantization to accelerate inference and decrease memory usage, and transfer learning using six relevant object classes (person, car, motorcycle, bicycle, bus, and truck). Experiments were conducted on a Raspberry Pi 5 equipped with a camera and local dashboard interface. The optimized MobileNet SSD v2 model achieved a mean Average Precision (mAP) of 0.724 and mAP@0.5 of 0.951, while improving inference speed from 21 FPS to over 24 FPS. These results indicate a balanced trade-off between accuracy, speed, and resource efficiency, enabling stable real-time performance on constrained IoT platforms. The findings contribute to the body of knowledge in embedded and edge AI by demonstrating how integrated model-level optimization can significantly enhance deep learning inference on low-power systems, offering scientific and practical implications for smart surveillance and intelligent traffic monitoring.