Fake Logo Detection System

This computer vision system implements a real-time object detection pipeline optimized for edge deployment on constrained hardware. The architecture leverages a lightweight convolutional backbone extracting hierarchical feature maps at multiple scales. These feature pyramids feed into a single-shot detection head that simultaneously regresses bounding box coordinates and class probabilities. The model is trained on a curated dataset of product logos, with extensive augmentation including photometric distortion, random cropping, and mosaic composition to enhance generalization. Post-training quantization reduces model size and inference latency without appreciable accuracy degradation, enabling deployment on mobile devices for real-time brand authentication and counterfeit detection at the point of sale.