To create an object detection system, start by defining the task and collecting a labeled dataset with bounding boxes. Use a deep learning framework like TensorFlow or PyTorch to train a model.
Pre-trained models such as YOLO, Faster R-CNN, or SSD can simplify the process. Fine-tune these models on your dataset, ensuring images are preprocessed (resized and normalized). Train the model with appropriate loss functions for classification and localization.
After training, deploy the system on a platform that suits your application, such as a web interface or edge device. Evaluate its performance using metrics like mean average precision (mAP) to refine the results.