The first step is determining the specific object detection task you want to focus on. Do you want to detect general objects like cars, people, dogs? Or do you want to focus on detecting a more specific set of objects like different types of fruit? Defining the problem clearly will help guide your model and dataset choices.

Once you have identified the target objects, your next step is assembling a dataset. You will need a large set of labeled images that contain the objects you want to detect. These labels indicate which images contain which objects. Some good options for starting are publicly available datasets like COCO, Labelme, OpenImages. You can also create your own dataset by downloading images from Google/Bing and manually labeling them. Aim for a few thousand labeled images to start.

With your dataset assembled, you need to choose an object detection model architecture. Some of the most popular and effective options are SSD, YOLO, Faster R-CNN and Mask R-CNN. SSD and YOLO models tend to be faster while Faster R-CNN and Mask R-CNN usually have better accuracy. I would recommend starting with a smaller YOLOv3 or SSD MobileNet model for speed and then experimenting with Faster R-CNN or Mask R-CNN once you have the basics working.

You will need to split your datasets into training, validation and test sets. I typically use 70% of images for training, 20% for validation during model training, and 10% for final testing once the model is complete. The test set should never be used during any part of model training or selection.

With your data split and model architecture chosen, you now need to load your dataset and train your model. This is where deep learning frameworks like TensorFlow, PyTorch or MXNet come in. These provide the necessary tools to define your model, load the datasets, set up training, and optimize model weights. You will need to configure things like the learning rate, batch size, number of epochs appropriately for your dataset size and model. Be prepared for training to take hours or days depending on your hardware.

During training, you should monitor the validation loss and accuracy to check if the model is learning properly or if it gets stuck. If accuracy stops improving for several epochs, it may be time to try reducing the learning rate or making other adjustments. Once training completes, evaluate your model on the held-out test set to assess final performance before deployment.

At this point, you will have a trained model that can detect objects in images. To build a full system, there are some additional components needed. You need a way to preprocess new input images before feeding to the model. This usually involves resizing, normalizing pixel values etc. You also need inference code to load the model weights and run predictions on new images in a smooth user-friendly way.

Frameworks like Flask, Django or streamlit are useful for creating basic web or desktop based interfaces to interact with your trained model. You can build a web app that lets users upload images which get preprocessed and fed to the model. The predictions returned can then be displayed back to the user. Things like drawing bounding boxes around detected objects help visualize what the model found.

For enhancing usability and performance further, some best practices include:

Using a model compression technique like quantization to reduce model size for faster inference on devices.

Optimize image preprocessing and inference code for speed using multiprocessing, GPUs etc.

Add non-maximum suppression to filter multiple overlapping detections of the same object.

Consider adding a confidence threshold to only display detections the model is very sure about.

Collect example detection results and gather feedback to continually refine the dataset and model. Misclassified examples help identify failure cases to improve upon.

Experiment with transfer learning by taking a model pretrained on a larger dataset and fine tuning it for your specific objects. This helps when data is limited.

For production, consider options like Docker containers, cloud deployment (AWS Sagemaker, GCP AI Platform etc) for easy scalability.

This covers the basic process of assembling a full end-to-end object detection pipeline from dataset creation to model training and deployment. With persistence in data collection, model experimentation and system refinement, you can develop very effective custom computer vision applications for specific domains. Let me know if any part of the process needs further explanation or guidance.