Vision Based Parking Slot Detection

Posted : admin On 4/6/2022
  1. Vision-based Parking Slot Detection Based On End-to-end Semantic Segmentation Training
  2. Vision Detection System
  3. Parking Slot Detection Deep Learning

The parking slot marking detection stage recognizes various types of parking slot markings. The parking slot occupancy classification stage identifies vacancies of detected parking slots using ultrasonic sensor data. Parking slot occupancy is probabilistically calculated by treating each parking slot region as a single cell of the occupancy grid.

Vision-based

To avoid unnecessary time conception to find an empty slot in a car parking area. To reduce the traffic due to the egeo-vehicles. To reduce the accidents inside the parking area and to find the path of the fixed slot. Recognition of the available slot at the entrance itself.

Vision-based Parking Slot Detection Based On End-to-end Semantic Segmentation Training

Vision-based parking-slot detection, a largescale labeled dataset is established. This dataset is the largest in this field, comprising 12,165 surround-view images collected from typical indoor and outdoor parking sites. For each image, the marking-points and parking-slots are carefully labeled. Due to the complex visual environment, such as lighting variations, shadows, and limitations of vision, the accuracy of vacant parking slot detection for the park assist system (PAS) with a standalone around view monitor (AVM) needs to be improved.

Vision detection systemBasedVision

To overcome the drawbacks of existing system, the sensor setups and control systems is to improve the performances of the system in standalone manner. Switching setup based sensor array for sensing the available parking slot. Limit switches are used as the sensing networks here. The information’s from the sensing network are passed to the control system. Control system monitor and control the slots using fuzzy logics and give information to vehicle section. The information from the vehicle section are transmitted over the ZigBee to the entrance and the output at the receiver end by the LED array for showing the status of the parking slot for visualization. Advantages •Unlike the free spacebased approach using ultrasonic sensors, this system can more accurately detect parking spaces because its performance does not depend on the existence and poses of adjacent vehicles. •Compared with the user interface-based approach via a touch screen, this system is more convenient since it requires no repetitive driver operations, and drivers only need to select one of the detected parking slots. •The ego vehicle can be more accurately settled at the target position because this system continuously estimates the position of the selected parking slot.

Slot

Vision Detection System

Detection of the parking slot and its occupancy in the parking area. This system consists of a sensor based networks and control system for the detection. The limit switches are used as the sensor networks. The limit switches are fixed on each parking area and it will indicate the vacant slot by ON OFF method. The information obtained from the sensor networks are controlled by the control unit. The pic controller is used as the control unit and the information’s are displayed on the LCD display in the parking area. Knowledge about the vacant slots can be updated frequently at the entrance of parking. The acquired information is transmitted to the entrance of the parking area. The transmission and reception of information can be done by the asynchronous serial communication using ZigBee. It gives the information to the users by the indication of slots at the entrance. The information is controlled by the control unit at the receiving area. The available and fixed slots are displayed on the LCD display. In the experiments, it is shown that the proposed method can recognize the positions and occupancies of various types of parking slot markings and stably track them under practical situations in a real-time manner.

Parking Slot Detection Deep Learning

  • ABOUT THE ENTRANT

  • Madhankumar S
  • team
    S.MADHAN KUMAR
    V.PRAVEEN
    P.S.PREM KUMAR
  • Student
  • MPLAB,PROTEUS,ZIGBEE
  • none