The ESP32-CAM lacks the computational power and memory to run YOLOv5 efficiently, causing slow processing and detection delays. YOLOv5 requires significant resources, which the ESP32-CAM cannot handle.

Solutions: Use a Lightweight Model – Try TinyML, MobileNet SSD, or YOLOv4-tiny, optimized for low-power devices.

Offload Processing – Stream video from the ESP32-CAM to a more powerful device (e.g., Raspberry Pi or cloud server) that runs YOLOv5.

Model Optimization – Use quantization and pruning, but even with optimizations, ESP32-CAM is unlikely to handle YOLOv5 effectively.

For real-time object detection, consider using an edge computing setup instead of running YOLOv5 directly on ESP32-CAM.

Reference: https://randomnerdtutorials.com/esp32-cam-opencv-js-color-detection-tracking/?utm_source=chatgpt.com