Our client, a very popular food delivery app, wanted to ensure the safety of their delivery bikers and wanted to make sure that they follow the traffic guidelines. They wanted to keep a close eye on their delivery riders who were riding without a helmet and were defying the traffic guidelines.
They asked Saffron Tech to develop a helmet detection system for their company that allowed them to identify delivery riders who violate their instructions. In this project, Saffron Tech aimed to build an Automatic Helmet Detection System that could detect helmet-less bikers using Machine-Learning and AI-based solutions.
This new system could detect riders and whether or not they were wearing a helmet. It also provided the client with real-time visuals of these riders. With the help of AI and ML, Saffron Tech was able to develop algorithms built on a mathematical model of sample data.
This is usually called “training data”, this is also used in several other applications that are adopted for ‘object detection’. By using the dataset, Saffron Tech was able to develop a Helmet detection model for the client. Moreover, this feature can be added to any handheld device or CCTV cameras.
The primary aim of this project was to provide a real-time helmet detection model to the client so that the administrative authorities of the company could easily monitor riders.
Through this feature, the client aimed to increase the safety of the riders and also enhance the delivery service manifold. Saffron Tech aimed to create a system that could be effortlessly installed and used in multiple devices and setups.
Saffron Tech proposed a ‘Helmet Monitoring’ system, that will detect whether a driver is wearing a helmet or not. The solution offered was powered by Artificial Intelligence and Machine Learning that allowed the clients to keep a check on their riders by providing them with real-time visuals.
Python Language – Saffron Tech used ‘Python code’ as it is well-suited for A.I. because Python makes the construction of AI models much simpler. It brings a broad choice of systems and libraries; it has also been used by popular brands like LensKart. It can perform an array of complex AI errands and help in constructing AI models at a rapid speed.
Deep Learning Frameworks – Python exhibits a diverse arrangement of libraries for computer reasoning and Artificial Intelligence. Therefore, for deep framework learning, Saffron Tech used Pytorch, Tensorflow, and Keras to base the feature.
Numerical Computation – We used NumPy for boosting information examination and for establishing optimum logical registering. We also used Pandas for information examination. The combination of NumPy and Pandas enabled us to bring our clients the ultimate structure they required.
Machine Learning – For the Machine Learning pipeline, we used sci-kit-learn, a learning library for Python Language which offered various dynamic tools for machine learning such as regression, classification, clustering, and more.
Image Processing – We operated image processing with OpenCV or Open Source Computer Vision Library because it provides more than 2500+ optimized algorithms. Skimage for image pre-processing. We also used PIL, to provide image editing functionalities.
DevOps – We used DevOps with Docker because it makes load balancing simpler with pre-installed service concepts, AWS for Cloud Formation and service integration, and GCP or Google Cloud Platform.
The system developed by Saffron Tech provided the clients with a visual examination of their riders in real-time. While delivering the orders, if their drivers fail to wear a helmet they can warn, instruct, or guide them accordingly. This also helped the food delivery businesses in reducing the chances of any road injuries to their drivers. Real-time detection based on AI and Machine Learning is being widely used in all kinds of businesses these days.
The database collected by the company can help them to get the data of violators and can allow them to take strict actions against such riders. The same system can be used at traffic lights, malls, and highways for monitoring traffic rule violations.