Over the past few decades vehicle detection devices have been used for different purposes. Vehicle detection is a crucial ITS task that attempts to give data for vehicle counting, measuring vehicle speed, identifying traffic accidents, predicting traffic flow, etc. Various sensors are employed to gather traffic data that is generated continuously. However there are some conditions when these traffic devices don’t tend to provide accurate information or their efficiency is affected.
In this article we will be discussing the cons of vehicle detection devices individually one by one and also the pros of using AI vehicle detection applications. These devices are mainly distributed into two main types: intrusive and non-intrusive technologies. Here we have some most commonly used vehicle detection devices:
- Infrared ( Active, Passive ):
There are infrared devices that can be mounted overhead to view incoming or outgoing traffic in a side-looking position. Data on vehicle presence at traffic lights, volume counts, vehicle lengths, and queuing measurements are provided by passive infrared detectors. Along with the information gathered by passive infrared devices, active infrared detectors are also able to provide speed readings. The biggest drawback is the lack of precision in weather situations like rain and fog since the resulting changes in air quality may affect how the infrared beam is reflected.
- Microwave (Doppler, Radar, and Passive Millimeter):
In most cases, microwave detectors are placed either immediately overhead or alongside the road. These tools are useful for measuring speed and volume. In urban traffic, they are known to falter, particularly at crossings with complicated geometries.
Additionally, because the broadcast waveform and antenna beam width must be appropriate for the purpose, the use of microwaves is restricted. In addition, stopped cars cannot be detected by Doppler sensors.
- Passive Acoustic:
An assortment of microphones pointed towards the traffic stream make up passive acoustic devices. They can be used to gather data on volume, velocity, occupancy, and classification. However, when it’s cold and snowy, they frequently count fewer animals.
- Ultrasound ( Pulse and Doppler):
Ultrasonic sound energy is released in short bursts by pulse devices, which time the signal’s return. However, some environmental factors, such temperature variations and intense air turbulence, can impact how well these detectors work. Large pulse repetition times may impair occupancy estimations on freeways for vehicles moving at moderate to high speeds, which is another shortcoming of this class.
- Video Image Processor:
The visual image input from a video camera is analyzed by a CPU in video devices. The key drawback is that the effectiveness of video detection is known to be impacted by a number of environmental conditions, including lighting, wind, and precipitation. Unfavorable weather, shadows, vehicle projection into adjacent lanes, occlusion, the change from day to night, the contrast between the car and the road, wind-driven camera shake, and water, salt, filth, grime, icicles, and cobwebs on the camera lens are all issues. As a result, maintaining a video image detector’s performance involves additional work. Additionally, because cameras must normally be mounted at heights of 50 to 60 feet, their use is limited in flexibility. The relatively high cost when more detection is required in a zone is another problem.
- Magnetic Detectors:
When a vehicle travels through a detection zone, passive magnetic devices measure the change in the earth’s magnetic flux that results. Extreme weather is known to have an impact on these intrusive gadgets. Stopped vehicles cannot be detected by passive magnetic devices.
- Inductive Loop:
A tiny electric current is applied to a coil of wires using active magnetic devices like inductive loops to detect changes in inductance brought on by the passing of a vehicle. Inductive loop detectors have the same limitations associated with intrusive devices. They include difficulties with safety for installation staff, disruption of traffic during installation, and damage to the road surface. Additionally, defective pavement surfaces and saw-cut water infiltration from rain have been linked to detector failures. Reinstalling the sensors can also be necessary for utility repairs and road resurfacing.
- Pneumatic Road Tube:
When a car’s tires pass over a rubber tube, the pneumatic road tube sensor delivers a burst of air pressure along the tube. An air switch is closed by the pulse of air pressure, creating an electrical signal that is sent to a counter or analysis programme. However, due to the physical characteristics of trucks and buses, the accuracy of such detectors is limited when the volume of these vehicles is considerable. The weather might also have a big impact on the gadget.
Vehicle Detection Device Vs Ai Vehicle Detection Applications:
Vehicle detection software will enable effective and painless identification and recognition of licence plates when vehicle detection sensors are so susceptible to weather variations and their performance might be impaired and there are risks of inaccurate data transfer through these devices. The detection software generates automation that maximises efficiency and has use cases across verticals and sectors, such as law enforcement or public administration. The solution is built to function in a variety of environments and use situations. Organisations and authorities can enhance their current systems and increase operational efficiency by leveraging the power of AI.
Since these ai systems automate vehicle recognition and counting while also recognizing the type/category of the vehicle, they are essential for road safety. They make it possible for you to easily monitor traffic and congestion and to make well-informed decisions to address significant inefficiencies. Key operations including research and development, maintenance, and safety regulation could be transformed by AI. In the end, this aids automakers in saving time and money while concentrating on those projects that have the greatest potential for growth in other crucial functional areas. In this regard Folio3 has been one of the most reliable firms that has a team of committed Data Scientists and Consultants at the machine learning business. Folio3 has completed end-to-end projects in machine learning, natural language processing, computer vision, and predictive analysis.
