Traffic-Sign Recognition

 Traffic signs are an important part of our road infrastructure because it provides critical information for road users, which in turn requires them to adjust their driving behavior to make sure they comply with road regulation enforced on the road sector.

Traffic sign recognition is a multi-category classification problem with unbalanced class frequencies. It is a challenging real-world computer vision problem of high practical relevance, which has been a research topic for several decades. Many studies have been published on this subject and multiple systems, which often restrict themselves to a subset of relevant signs, are already commercially available in new high- and mid-range vehicles. Nevertheless, there has been little systematic unbiased comparison of approaches and comprehensive benchmark datasets are not publicly available. 

Related work

It is difficult to compare the published work on traffic sign recognition. Studies are based on different data and either consider the complete task chain of detection, classification and tracking or focus on the classification part only. Some articles concentrate on subclasses of signs, for example on speed limit signs and digit recognition.

How Does TSR Work?

Traffic-sign recognition is a safety tech system that recognizes traffic signs and relays the information displayed on the sign to the driver through the instrument cluster, infotainment screen, or head-up display. Most TSR systems can identify speed limit, stop, and “do not enter” signs. More sophisticated systems may be able to recognize other types of signs.

The primary purpose of TSR is to increase driver focus. If a driver misses a sign, TSR can make them aware of it so they can react accordingly. The idea is simple: TSR identifies road signs the driver might have missed and alerts them of their presence.

This technology uses advanced forward-facing cameras positioned high on the windshield, generally adjacent to the rearview mirror housing. Aimed to “see” traffic signs, the cameras scan the side of the road relative to the car.

Once the camera captures a sign, the system’s software processes the image to establish its classification and meaning. The system then relays this information to the driver almost instantaneously in the form of an icon or graphic representation of the sign. However, TSR’s ability to accurately identify a sign depends on the speed of the vehicle and its distance to the sign.

Some TSR systems also work in conjunction with advanced cruise control, which is set to maintain a speed above or below the scanned signs. For example, if TSR detects a 40-mph speed limit, it updates the cruise set speed to 40 mph unless the driver sets the parameters above or below the detected speed limit.

Aside from cruise control-related functionalities, TSR may use the same forward-facing ADAS camera that tracks lane markings to inform the vehicle’s lane-departure warning system or distracted driver alert system. So, it’s common for these features to come with TSR in the same ADAS package. 

The Limits of TSR

Current TSR technology cannot determine all traffic signs or operate in all conditions. Several circumstances limit the performance of TSR systems, including:

•         Low visibility due to poor weather (fog, snow, heavy rain, etc.)
•         Dirty or improperly adjusted headlights
•         Foggy or blocked windshield
•       Warped, twisted, or bent signs
•         Abnormal tire or wheel conditions
•         A tilted vehicle due to a heavy load or modified suspension

While TSR and similar camera- and sensor-based technologies are potentially significant to moving us toward an autonomous driving future, there is still a long way to go. At this point, TSR is still primarily a driver-assistance system meant only to assist. Drivers cannot rely solely on this system to make steering or braking maneuvers/adjustments.

TSR Calibration

Forward-sensing cameras are not self-calibrating. Professional calibration is necessary for most ADAS to work correctly following an accident, windshield replacement, suspension work, or alignment changes. During calibrations, technicians realign forward-facing cameras to the proper position.

Which Car Brands Offer TSR?

Because of how new this innovative safety technology is, not many auto brands include TSR as standard or optional equipment on their models. Premium brands like Audi, BMW, and Mercedes-Benz commonly offer TSR on their models, while safety stalwart Volvo provides the technology on every model in its lineup. While it is less common among mainstream brands, several of them, including Ford, Honda, and Mazda, also offer TSR as part of their higher-level ADAS packages on specific models.

Bibliography:

•         https://www.sciencedirect.com/science/article/abs/pii/S0893608012000457?casa_token=UpuxUSiXtowAAAAA%3AG3VtKs2vaA7qfaCd118GBHQyCyDWtYYPYHvY56Yno3YxYC_KkZXSKRO0J9o6a18VKwQ8qEB-gDU
•         https://www.researchgate.net/publication/346250677_Automatic_Traffic_Sign_Recognition_Artificial_Inteligence_-_Deep_Learning_Algorithm
•         https://www.jdpower.com/cars/shopping-guides/what-is-traffic-sign-recognition#:~:text=Traffic%2Dsign%20recognition%20is%20a,%E2%80%9Cdo%20not%20enter%E2%80%9D%20signs

Artificial Intelligence to Manage Food Waste

    According to statistics, almost one third of all food produced in the world every year goes uneaten due to a lack of resources and infrastructure. AI can improve this growing problem of food waste by automating processes that have been traditionally manual, such as sorting out leftover food at restaurants or managing inventory across multiple locations.

    It's also ideal for automated forecasting to predict how much product will be available based on demand patterns that take into account factors like seasonality and weather patterns so businesses don't run out of stock due to bad timing.

In the fight against food waste, especially in restaurants and other large food production chains where there is a high volume of products coming out of every single kitchen, it's easy for workers to ignore small amounts of unsold food and even when they're aware that something isn't selling well, they may not know what exactly needs to be done about it.

A simple model could automatically detect when products aren't meeting demand, and then alert managers so that things can be fixed before too many products get thrown away.

Reduce food waste in the supply chain

    AI can help us monitor food waste in the supply chain. It can also help identify the causes of food waste, predict it, and provide solutions for reducing it. For example, an AI system could be used to detect when products are nearing their expiration date and then alert suppliers about these issues so that they can be addressed before they become more widespread. If you're a retailer or distributor who wants to reduce your losses from spoilage due to improper storage temperatures or other factors, an automated system could alert you when certain items start showing signs of distress so that you can take action quickly.

Predict food shortages for farmers

Models used to predict food shortages can also help farmers plan for future shortages. The model learns from past data and then uses that information to predict how much food will be available in the future. Farmers can use this to decide which crops they want to grow and when they should harvest them.

Reduce food waste in restaurants

AI can help predict customer demand, which means restaurants will be able to plan for food that's likely to sell out. It also helps chefs understand how people like their food and what they want next time they visit the restaurant. This allows them to create menus that are more customized for each individual customer, reducing overall waste by avoiding unnecessary ingredients or serving leftovers at peak times of day when there isn't much demand.

Restaurants will be able to predict preferences based on previous orders, if someone has ordered fish twice before but only once this time around, this information would allow them to reduce their use of fish again without sacrificing quality standards.

AI has the potential to solve a lot of problems in our world, and food waste is one that we should be working on. AI can help us make better decisions about what to eat and when, as well as how much of it needs to be eaten.

Bibliography:

https://www.relexsolutions.com/resources/how-ai-can-fight-the-food-waste-battle/

https://www.fraunhofer.de/en/press/research-news/2021/april-2021/artificial-intelligence-for-reducing-food-waste.html

Prediction of molecular properties and drug targets

It is well known that machine learning is widely used in bio-metrics and health care systems, however the prediction of molecular properties and drug targets domain is not so developed. Now overall success rate of drug discovery and preclinical studies is around 0.05% - 0.1%. There are emergencies when the medicine should be discovered as fast as possible, for example pandemic situation. Or there are disease for which the drug couldn't be found for years, for example Parkinson’s disease and Alzheimer’s disease. Therefore, there is a need for rapidly and accurately discovering drugs.

There are three main reasons why machine learning can be used for prediction of molecular properties and drug targets:

1. There exists a powerful information, databases which can be used for learning. UniProt is supported by many institutions, and is the most informative and comprehensive protein database (Consortium, 2015).
2. There are powerful toolkits and web servers which can help to solve problems in drug–target interaction prediction. One of these tools is OpenChem which is a pytorch-based deep learning toolkit for computational chemistry and drug design, which contains Feature2Label, Smiles2Label, Graph2Label, SiameseModel, GenerativeRNN, and MolecularRNN. Users can train predictive models for classification, regression, and multi-task problems, and develop generative models for generating novel molecules with optimised properties.
3. Current status and requirements. There are still a lot of things to be discovered, and many of them cannot be done without the help of computers. For example the human genome contains more than 20.000 genes, and approximately 80% of them can encode one or more proteins. Only a small number of proteins have been identified as pharmacologically active and are targets for currently approved drugs.

A recent research propose a pre-trained model ImageMol which is used to predict molecular targets of candidate compounds. The ImageMol framework demonstrates a high performance in evaluation of molecular properties (that is, the drug’s metabolism, brain penetration and toxicity) and molecular target profiles (that is, beta-secretase enzyme and kinases) across 51 benchmark datasets.

ImageMol shows high accuracy in identifying anti-SARS-CoV-2 molecules across 13 high-throughput experimental datasets from the National Center for Advancing Translational Sciences. Via ImageMol, it was identified candidate clinical 3C-like protease inhibitors for potential treatment of COVID-19.

ImageMol model combines an image processing framework with comprehensive molecular chemistry knowledge for extracting fine pixel-level molecular features in a visual computing way. It has several big improvements compared to other applications:

• It utilises molecular images as the feature representation of compounds with high accuracy and low computing cost;
• It used a wide dataset of images for training. A molecular encoder is designed to extract latent features from ~10 million molecular images.
• Five pretraining strategies are utilised to optimise the latent representation of the molecular encoder by considering the chemical knowledge and structural information from molecular images.
• A pretrained molecular encoder is fine-tuned on downstream tasks to further improve model performance. 

In conclusion I would like to say that computational approaches and technologies could be considered a promising solution which can substantially reduce costs and time during the complete pipeline of drug discovery and development.

bibliography:

• https://www.nature.com/articles/s42256-022-00557-6#Abs1
• https://jcheminf.biomedcentral.com/articles/10.1186/s13321-020-00447-2

AI Generated Code: an Aid or a Threat to Software Developers?

During recent years, many low-code/no-code solutions gained popularity as alternatives to hiring software developers for creating simple websites, like e-commerce platforms for businesses. In this article we are talking about the latest trend in code generation, OpenAI's GPT chatbot.

GPT-3, (which stands for Generative Pretrained Transformer 3) is a language model that has the ability to generate human-like text. GPT-3 is trained on a large dataset of text and can generate text that is coherent and similar to human writing. Since the public launch of GPT, tech enthusiasts have explored its capabilities of code generation and found that it can create complex pieces of code. This raises the question: can GPT-3 replace programmers? The short answer is no. While GPT-3 can generate text that resembles code, it is not capable of generating functional, efficient, or reliable code. For proving this point, I decided to test the generator myself on a few practical problems, and analyze the results. I selected some problems from books and contests for beginners in competitive programming. At first I was impressed by its capacity to come up with custom implementations in my languages of choice for variations of classic algorithms. For example, it succeeded in implementing a functional React component for multiplying matrices provided by the user, or a C++ function for applying Dijkstra's algorithm, assuming that the cost of a path is the maximum of the edges instead of their sum. However, when the problem did not only require an adaptation of a classic algorithm, but rather expected an original approach, the AI failed. This is because generating code requires a deep understanding of the problem that is being solved and the context in which the code will be used, which are difficult for AI algorithms to replicate. In addition to this, writing code is more than just stringing together lines of code – it requires problem-solving, critical thinking, and creativity. As a conclusion, I think that, while GPT-3 or similar solutions may be able to assist programmers in certain tasks, they cannot replace them entirely. Programming will continue to be a critical skill for developers, and the human touch will remain an essential element of successful code development. GPT-3's code generating abilities are limited, and it cannot replace human programmers.

Bibliography: Introduction - OpenAI API