Mobile Real-Time Object Detection with SSD MobileNet

Recent advancements in deep learning have significantly transformed object detection, surpassing conventional methods such as Haar cascades and Histogram of Oriented Gradients (HOG) in accuracy, efficiency, and adaptability. This paper presents a systematic study of deep learning-based object detection techniques, with a focus on real-time implementation for mobile platforms. A comparative analysis highlights the strengths of modern architectures, including Faster R-CNN, You Only Look Once (YOLO), and Single Shot MultiBox Detector (SSD) with MobileNet, over classical approaches. The primary contribution of this work is the design and evaluation of an optimized SSD MobileNet-based object detection system for Android, utilizing TensorFlow Lite for efficient on-device inference. Experimental results demonstrate that the proposed solution achieves real-time performance while maintaining robust detection accuracy. Key challenges, such as computational latency, model size constraints, and multi-scale object variability, are addressed through techniques like post-training quantization and hardware acceleration. This study serves as both a technical reference and a practical guide for implementing real-time object detection on resource-constrained devices, offering insights into trade-offs between speed and precision.