Digital Transformation
One Solution At A Time!

Our AI-powered solutions have served clients from a wide range of industries. With seamless integration between our AI ecosystem and enterprise hardware, rapid product development capabilities and unified vendor management, our solutions bring the potential of digital process automation to your fingertips!
We have successfully deployed various solutions, including:

Gunshot And Suspicious Sound Detection System

The Challenge: 

Wipro Limited is one of the biggest technology firms in the world and provides information technology, consultancy and business process services. The client wanted an AI-powered system which could detect various sounds, such as that of a gunshot, car horn, glass breaking, human yelling, etc., and generate real-time alerts as well as take images via cameras in the premises.

Our Solution:

We used specialized edge computing cameras with audio recording features with in-built AI models that were trained to process sounds and accurately detect suspicious sounds such as gunshots, vehicle horn, glass breaking, etc. These devices would be installed in and around the business premises to secure the area.

These cameras would be connected to on-premises AI processing servers to analyze images and detect the target sounds. As the cameras are connected to the EaseMyAI platform via AI servers, on detecting suspicious sounds, the AI solutions sends alerts to concerned authorities, security teams, etc. and also sends sound recordings/camera images to the authorities through the EaseMyAI platform, or other communication channels, including BlackBerry AtHoc.

Using the EaseMyAI solution, the client could configure its use during emergencies to send instant alerts and footage/audio from cameras to law enforcement agencies and other authorities. Additionally, gunfire alerts can be pushed via the EaseMyAI platform, mobile apps, desktop apps or browser apps. The entire transaction – from initial sound detection to sending the alert – happens in under 30 seconds!

ALPR (Automatic License Plate Recognition), Road Safety And Perimeter Security Systems

The Challenge: 

SAIL is a public sector undertaking owned by the Ministry of Steel, India.
The client wanted to enhance the security, safety and operational efficiency at the Rourkela Steel Plant by implementing video analytics solutions within its premises.

One demand was to implement an automated vehicle license plate recognition system using the existing CCTV network at the plant to capture and analyze number plates in real-time.

The client also needed a video analytics system that could integrate with the CCTV system to recognize traffic light violations at key intersection within the plant premises.

To ensure a high standard of security, the client also wanted an AI-automated system to detect unauthorized intrusions at the plant perimeter, and a gas leak detection system as well.

Our Solution:

By leveraging computer vision, video analytics and advanced AI algorithms, we provided the client with a data-driven, real-time monitoring system to extract valuable insights from CCTV cameras, video footage, and sensors.

Automated License Plate Recognition

Traffic Signal Violation Detection

Intrusion Detection

Gas Leakage Detection

Initially, the CCTV cameras use optical character recognition (OCR) technology to extract number plates from the video footage. Then, the computer vision model compares the captured number plates against predefined blacklists and whitelists to identify if the vehicle is authorized to enter the plant. Finally, the system generates real-time notifications to alert the command center when a blacklisted vehicle is detected, enabling prompt intervention.

We implemented a video analytics-based traffic signal violation detection solution at key intersections within the plant premises. The system analyses the real-time video feeds from CCTV cameras installed at key traffic junctions to identify traffic violations. The solution can also identify vehicles that enter an intersection during the red signal and capture video evidence for further action using the ALPR solution mentioned above.

By using video analytics, we deployed a solution to monitor and secure the perimeter of the steel plant. The solution analyses video feeds from CCTV cameras mounted on the boundary walls to identify potential intrusions. The clients can define virtual perimeters, ensuring that any movement beyond the set boundaries triggers an alarm to notify security teams. On top of that, by utilizing advanced computer vision techniques, the solution can differentiate between humans and other objects. The ability to receive instant alerts and notifications allowed the security team to respond and intervene swiftly.

By installing gas leakage detection sensors across 350 locations within the plant premises, and integrating them to an AI-driven analytics system, we developed a solution to detect gas leakages. The solution allows the client to configure the system based on gas leakage parameters for each location, such as gas concentration levels or abnormal changes in readings. When the conditions are met, and a gas leakage is identified, the solution triggers alarms/notifications to enable real-time response for prompt evacuation.

Object Counting And Loading Solution

The Challenge: 

Yokohama Tires is a globally renowned tire manufacturing company with several warehouses, including one in Dahej. With a significant volume of tires being shipped and received daily, loading the tires into containers requires utmost attention to avoid missing any tire while loading.

Missing a tire while loading the containers can lead to loss of revenue, delays in delivery, and an increased workload. Therefore, the client wanted to implement a computer vision-based tire counting and loading solution to make the process more accurate and efficient.

Our Solution:

We designed and implemented a computer vision-based solution for Yokohama Tires’ Dahej warehouse. The computer vision solution relies on cameras installed at strategic locations, such as the loading bays, to capture images of the tires being loaded into containers. An artificial intelligence software, trained to detect tires in the images, processes the images from the cameras in real-time to identify if any tires are missed or present in the forbidden zone while loading.
Moreover, our solution notifies the operators with visual and audio alarms. This allows the operator and managers to take appropriate action immediately, avoiding potential delays in delivery and loss of revenue.

Railway Track Defect Monitoring

The Challenge:

It is critical to identify any defects in railway lines, especially those used by ports and large warehouses on a daily basis. Hence, the client wanted an automated system that could not only help in identifying such defects on railway tracks, but also pinpoint the location for quick repairs.

Our Solution:

In order to continuously monitor the railway track, we created and installed a special hardware device on the brake van (the last coach of a train).

During operations, the dedicated camera with integrated computer vision scans the railway track’s clips.

After real-time image processing, if any clip is found to be missing from the track, the built-in GPS module is activated to extract the precise geo-location of the missing clip.

Visual Positioning System

The Challenge:

The client wanted an AI-powered system to accurately identify the position of individual items within a warehouse that could reduce the time taken to locate items, and consequently streamline their inventory management strategies.

Our Solution:

To calculate the exact geolocation (latitude & longitude) of items, we designed a platform that could map the location of objects on Google Maps, providing a simple and familiar user interface.
For the hardware setup, we deployed PTZ (pan, tilt and zoom) cameras as well as fixed cameras that could track the forklifts’ movement and monitor the position of each rack within the warehouse.

The PTZ cameras would continuously scan the forklifts’ movement in their respective visual zone. Once a camera detected a loaded forklift near the stacking area for at least 30 seconds, our computer vision solution would calculate the exact position of the forklift (latitude and longitude) using the camera’s location.

Once the forklift operation is completed, the PTZ camera can zoom into the rack and find the QR Code associated with that rack. Once the QR Code is detected, the camera can read the QR Code and store precise information about the rack and its position directly into the warehouse database.

Similarly, other cameras will scan QR Codes of other racks to store item-position information on the database. When a user wants to find a particular item, they can simply use the solution to enter the item details into the search bar. The system searches the database and fetches complete information about the particular item, including its exact position on the rack and geo-location with a spatial resolution of 5 centimeters.

Occupancy Detection Using Thermal Camera Array

The Challenge: 

The security teams at airports are always on the lookout for suspicious individuals or behaviour to identify possible illegal activities. However, using traditional cameras is not possible due to GDPR restrictions, which ensure that the privacy of individuals is maintained and no personal information is captured during occupancy detection.

Our Solution:

Our Solution: We deployed an array of IoT thermal cameras in areas of the airport where traditional cameras were prohibited. The image data from the IoT thermal camera array is then processed by our computer vision solution in real-time to detect the presence and number of people in the area.

To comply with GDPR regulations, our solution preserves the privacy of individuals by not capturing any personal identifiers. The client used it to detect suspicious behaviour, such as a person was loitering in certain areas for more than the specified time. 

This occupancy detection system helped reduce instances of gold smuggling at Sri Lankan airports, while respecting the privacy of individuals being recorded.

Automated Textile Defect Check

The Challenge:

Quality Control (QC) is paramount in any production-based industry.
The client wanted an automated computer vision solution to detect and categorize textile defects in real-time to streamline their QC process.

Our Solution:

To automate the quality control process, we set up hardware including high-speed USB cameras and a PLC-based label applicator, and connected them to our AI platform. Next, we deployed AI models on the platform that could visually identify 45 different types of textile defects.

As the textile product progressed on the production line, the USB cameras scanned it, and if a defect is found, the platform triggered the PLC label applicator device to apply a relevant label to the defective cloth. Moreover, the status of the defect identification process is displayed on the dashboard. This helped the client automate the entire end-to-end Quality Control (QC) process for their textile product.

Truck Unloading Tilt Detection

The Challenge:

Port and logistics businesses often face issues while loading/unloading containers from trucks. One common issue is that, on occasion, the RTG crane operator tries to lift the container off the truck before the locks of the container are opened.

Due to the non-opening of the container locks, the truck gets tilted and accidents may potentially happen, causing damage to the truck. In the other scenario, when RTG crane operators are loading containers on trucks, aligning the container with the truck bed takes time, and any misalignment can damage the truck or the container.

Hence, the client wanted a solution to detect if the truck is being lifted from the ground during unloading using computer vision and alert the crane operator to stop the operation immediately. Similarly, the client wanted a solution to help automate the alignment of the container and truck bed during loading of containers.

Our Solution:

Unloading Operation Solution: We deployed LIDAR (Light Detection and Ranging) sensors on the sides of the RTG crane to align with the truck’s position. Using computer vision integrated cameras, the solution can accurately measure the tilt of the truck in case the crane lifts the container before the mechanical lock between the truck chassis and container is opened. Moreover, as soon as the truck tilts beyond a threshold, our solution alerts the crane operator and simultaneously activates a hooter in the crane operator’s cabin. To automate the stopping of the operation, the solution even provides a “STOP” signal to the PLC of the crane.

Loading Operation Solution: We developed a solution by setting up a specialized camera on the crane that aligned exactly with the top of the truck. The computer vision solution then calculates a virtual region, within which the truck must be aligned. The virtual region is visible to truck driver on a screen inside his cabin. Unless the truck driver aligns the truck bed to the virtual region, a red outline will be displayed on the screen along with an instruction to align the truck. During this time, crane operator is also instructed to stop (or slow down) to movement of the container towards the truck.

Once the truck is aligned within the virtual region, the virtual box has a green outline and the crane operator gets an alert on his screen to resume the movement of the container towards the truck. If the operator still continues to move the container towards the truck before it is aligned, our solution sends a command to the crane’s PLC system and stops the operation, while alerting the operator with a hooter.