Current Use

Deep learning is being utilized in numerous fields such as automotive, aerospace, manufacturing, electronics, medical research, and other fields. According to Amazon, “Self-driving cars use deep learning models to automatically detect road signs and pedestrians. Defense systems use deep learning to automatically flag areas of interest in satellite images. Medical image analysis uses deep learning to automatically detect cancer cells for medical diagnosis. Factories use deep learning applications to automatically detect when people or objects are within an unsafe distance of machines.” AWS. (2023). These are some of many applications for it. Further use examples are content moderation, speech recognition, natural language processing for systems, recommendation engines, real estate, or dealership market.

As per the Frontiersin.org “Deep learning has been successful in the field of image processing: it carries out tasks such as image classification, target recognition, and target segmentation by analyzing images. Therefore, the application of deep learning in the task of medical image analysis has become a trend in medical research in recent years. Researchers used the artificial intelligence methods to help physicians make accurate diagnoses and decisions. Many aspects are involved in these tasks, such as detecting retinopathy, bone age, skin cancer identification, etc. Deep learning achieves expert level in these tasks. The convolutional neural network is a powerful deep learning method. The convolutional neural networks follow the principle of translational invariance and parameter sharing, which is very suitable for automatically extracting image features from the original image.” (Yang et al., 2021)

Recently, DL is being utilized to effectively detect COVID-19 in patients. As per the article “Wasserstein GAN based Chest X-Ray Dataset Augmentation for Deep Learning Models: COVID-19 Detection Use-Case,”, the researchers show that the use of a Wasserstein Generative Adversarial Network (WGAN) could lead to an effective and lightweight solution. It is demonstrated that the WGAN generated images are at par with the original images using inference tests on an already proposed COVID-19 detection model. They used “WGAN-based technique to synthetically generate training images for better development of deep learning models in diagnosing COVID-19.” (Hussain et al., 2022)

Other than that deep learning is being used to diagnose various diseases ahead of time such as Tuberculosis, Alzheimer etc.

 Another great use of deep learning is in automation. A recent application of deep learning was done by researchers in Korea to detect unexpected accidents under bad CCTV monitoring conditions in tunnels. The system can detect all accidents within 10 seconds. This is a practical system that works by “The vehicle object tracking algorithm tracks the vehicle object by changing the tracking center point according to the position of the recognized vehicle object on the image. Then, the monitor shows a localized image like a bird’s viewpoint with the visualized vehicle objects, and the system calculates the distance between the driving car and the visualized vehicle objects. This process of the system enables to objectively view the position of the vehicle object so that it can help assistance of the self-driving system. As a result, it can localize the vehicle object in vertical 1.5m, horizontal 0.4m tolerance at the camera.” (Lee & Shin, 2019)

Another deep learning-based detection system they developed was to estimate the speed of the vehicle, classify vehicle type, and analyze traffic volume. This was “developed to monitor moving vehicles on urban roads or highways by satellite. This system extracts the feature from the satellite image through CNN using the satellite image as an input value and performs the binary classification with SVM to detect the vehicle BBox.” (Lee & Shin, 2019)

Some other uses include automation in factories such as defect detection system to improve production efficiency and quality. As per the article “The Internet technology for defect detection system with deep learning method in smart factory” published in IEEE, “the proposed system is used to detect the machining defect of steel semi-finished products. In our experiments, the size of the machining defect for steel semi-finished products exceeds 1mm can be detected successfully.”  (Huang et al., 2018)

 Moving on, lets focus on some security aspects of the deep learning.