Facial Emotion Detection

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—specifically a MobileNet or EfficientNet variant—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 everyday objects, 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 Raspberry Pi-class devices for applications such as inventory counting, security perimeter monitoring, and assistive navigation for the visually impaired.