Machine vision technology is an integral significant factor in the automotive industry and the transportation sector in general. By utilizing various cameras, autonomous vehicles can accurately identify and understand their surroundings, including recognizing traffic signs, obstacles, and pedestrians, enabling safer driving. Additionally, computer vision improves the maintenance of vehicles and transportation systems by detecting potential faults earlier, consequently reducing costs and enhancing safety. In the area of traffic law and parking enforcement, computer vision enables real-time identification and analysis of traffic violations such as running red lights or parking in restricted areas which increases enforcement efficiency and leads to a reduction in the number of violations. Furthermore, computer vision optimizes the management of urban transportation infrastructures by monitoring traffic and intelligently directing traffic flows, resulting in reduced traffic congestion and overall improvement in transportation efficiency.
The Need
The client is a company that provides road monitoring and mapping services. Part of the mapping process includes the identification and GPS location of road signs along the monitored road. The number of signs is very high, and an automated system is needed to handle the frequency, diversity, and quantity of signs in the entire area.
The Challenge
The Solution
Design and development of a software module that operates on the client’s control vehicle. The module uses the images captured by the camera already installed in the vehicle and extracts all the information from them in real time. Sign recognition includes the deciphering of information such as the sign’s definition, sign category, and OCR in cases where this data is significant (distance, city names, instructions). Additionally, each sign is tagged with its geographical location data. The system is designed to operate in various weather and lighting conditions.
The Need
The client is a company that provides many services to local authorities. To expand their service portfolio, they are interested in automating parking control along city roads. The idea is that after registering for the service, any vehicle can park freely without the need to report or activate anything. Additionally, data about available parking spaces throughout the city will be accessible to drivers, and additional data about traffic such as congestion, noise, air pollution, and traffic violations can be collected and reported in real time to the municipal control center.
The Challenge
The Solution
Design and development of a unit with multiple cameras that can cover the controlled road. The unit is responsible for identifying the license plates of all vehicles entering the area and tracking them until they stop at a parking space. The unit communicates with other units and with a control center where all data is collected and displayed. Additionally, we added sensors for noise and air pollution, and this data is also reported to the municipal center. Additional challenges we faced included the need for the system to operate on solar energy, independent of the power grid, and therefore it was designed with very low power consumption using NVIDIA processors and smart algorithms. Another challenge was the desire to minimize the number of units needed to cover each area, so the optical design of the unit was developed to cover long distances and adaptation capabilities to a variety of street layouts.
In today’s advanced industrial world, smart cameras play a critical role in enhancing accuracy, efficiency, and quality control. However, finding an industrial camera that delivers
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