Efficient Eye-based Drug Testing for Taxi Drivers

(UNET Implementation)

Summary:

This blog explores the creative application of a UNET paradigm for drug testing of the eyes among taxi drivers in Columbia. Through examination of the eye's pupil and iris regions, the method provides a novel approach that measures diameter and radius to precisely determine drug addiction. This low-cost solution not only transforms the way that conventional drug tests are conducted, but it also helps taxi drivers in Columbia who are facing financial difficulties because the usual tests are both costly and ineffective. By incorporating state-of-the-art technology, this system offers a feasible substitute, guaranteeing the well-being and security of cab drivers while maximizing resources in the healthcare system.

Background and Motivation:

Traditional drug tests, which frequently produce unfavorable results, are expensive and inconvenient for taxi drivers in Columbia. A more accessible and affordable option is to use a UNET model for eye-based testing.

Problem Statement:

For Columbia's taxi drivers, the expense and inconvenience of using conventional drug testing procedures is a major obstacle. This problem requires a more affordable and easily accessible solution.

Methodology:

The methodology uses a UNET model to implement the eye-based drug testing system in a methodical manner.  First, image and mask data for training and validation are handled by a custom dataset class called `CustomDataset`. Preprocessing, transformation, and data loading methods are covered in this class. Training and validation sets of the dataset are separated, and each has associated image and mask folders. The input images undergo various transformations, including tensor conversion and resizing. 

Next, the Deeplabv3 ResNet50 model—the UNET model architecture designed for binary segmentation tasks—is instantiated. The model is trained using a loss function called `torch.nn.CrossEntropyLoss()` and an optimizer called `torch.optim.Adam()}. During a predetermined number of epochs, the training loop iterates and trains the model using batches of the training dataset. Furthermore, the model's performance is assessed using the validation dataset to track training development and avoid overfitting. Metrics like training and validation losses are tracked during the process for analysis. Lastly, an evaluation of the trained model using unobserved data determines how well it works for eye-based drug testing.  This process guarantees that the UNET model will be implemented in an organized and thorough manner for the intended application.

UNET Model Architecture and Working

Here is the model architecture I have used the DeepLab v3 ResNet 50 version. 

Following are the generated results of the trained model over the test samples of different people.

Extraction of the measurements in values

:

Input Image and its response in JSON

Limitations and Recommendations:

Despite its effectiveness, the eye-based drug testing system has limitations. Further exploration is recommended to enhance accuracy and accessibility. While the UNET-based system offers a promising solution, limitations such as accuracy and reliability need to be addressed. Further research and development are recommended to improve the system's performance and accessibility.

Conclusions:

In conclusion, implementing a UNET model for eye-based drug testing presents a cost-effective and efficient solution for taxi drivers in Columbia, addressing the challenges associated with traditional testing methods. Explore the implementation of a UNET model for eye-based drug testing among taxi drivers in Columbia, offering a cost-effective alternative to traditional methods.

Dataset + Model Link: Mege Drive Link