Parking Space Management

Date: 2021-6-21

Author: 강지훈, 도경민

Github: https://github.com/chloerudals/DLIP_LAB4

Demo Video:

Introduction

Since the use of private vehicle has increased due to COVID-19, finding parking spaces has been difficult even in our university. Thus, we decided to show the empty parking spaces on the screen to make parking management easier.

To watch a short explanatory video. Click Here

Algorithm: 1. Parking lines are detected using HoughlinesP and cars are detected using Yolov5s 2. We improved the detection of parking lines, which had previously been covered by parked cars, by elongating the lines 3. We divided the rectangle the same number as the parking lot. 4. Adjusted distorted regions due to perspectives. 5. By comparing the center of the parking space and the center of the detected box, parking ability is decided. 6. Since cars park in the upper part of the parking space, y axis of the detected box's center is corrected about 10 pixels 7. If a car comes in the camera frame, the car is considered as parking so entering car is printed.

Requirements

Hardware
- NVDIA graphic cards
Software
- CUDA
- cuDNN
- Anaconda
- YOLO V5

Or You can follow the instructions from the yolov5 GitHub repository. (requirements)

Follow the steps for setting YOLO V5 in the Anaconda.

Before starting, check your gpu to match the version.

  # create a conda env name=yolov5 (you can change your env name)
  conda create -n yolov5 python=3.8
  conda activate yolov5
  
  # Installation process
  conda install cudatoolkit=10.2
  conda install cudnn
  conda install tensorflow-gpu=2.3.0
  conda install keras
  conda install pillow
  conda install matplotlib
  conda install opencv
  
  # clone yolov5
  git clone https://github.com/ultralytics/yolov5.git
  
  # update
  conda update -yn base -c defaults conda
  
  # install Lib for YOLOv5
  conda install -c anaconda cython numpy pillow scipy seaborn pandas requests
  conda install -c conda-forge matplotlib pyyaml tensorboard tqdm opencv

  # install pytorch
  conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch
 
  # Extra
  conda install -c conda-forge onnx

Essential codes to understand the program.

Finding the parking lines.

First, you need a parking lot's picture to detect the parking lines. (an empty parking lot image would be perfect.)
Image processing is conducted.
Since the lines are mostly white and yellow, select only yellow and white colors from the image.
def select_rgb_white_yellow(image):

Convert the image to gray scale.
  def convert_gray_scale(image):
Detect the edges with Canny.
  def detect_edges(image, low_threshold=500, high_threshold=1000):

Crop the image using roi.
def filter_region(image, vertices): def select_region(image):
Using HoughlinesP, detect the vertical parking lines.
def hough_lines(image): def draw_lines(image, lines, color=[255, 0, 0], thickness=2, make_copy=True):
Draw a rectangle.
def identify_blocks(image, lines, make_copy=True):
Adjust the rectangle.
Merge the rectangle with the adjusted verticle lines to delineate the parking lines.
Count the total parking spaces.
def draw_parking(image, rects, make_copy = True, color=[255, 0, 0], thickness=1, save = True):
Assign a number to the parking spaces.
def assign_spots_map(image, spot_dict=final_spot_dict, make_copy = True, color=[255, 0, 0], thickness=2):

To see a detailed explanation. Click Here

Detect cars.

Car detection is done by YOLO V5s with COCO datasets.

Distinguish whether the parking space is empty or not.

Firstly, find the centers of the parking space and the car.
If the distance between the centers are less than 40, the parking space is determined as occupied.

for *xyxy, conf, cls in reversed(det):
    bx1, by1, bx2, by2 = xyxy[0], xyxy[1], xyxy[2], xyxy[3]
    bcx, bcy = abs(bx2 + bx1) / 2, abs(by2 + by1) / 2 + 10
    cv2.circle(im0, (int(bcx), int(bcy)), 5, (255, 255, 255), 2)
    parking_distance = math.sqrt((bcx - pcx)**2 + (bcy - pcy)**2)

    if parking_distance < 40:
        cv2.polylines(im0, [poly_points], 1, [0, 0, 255], 2)
        cv2.line(im0, (int(bcx), int(bcy)), (int(pcx), int(pcy)), (255, 255, 255), 2)
        cv2.putText(im0, "%d" %spot_cnt, text_coordinate, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 3)
        Parked_Space_Array.append(spot_cnt)
        break

Editing Parser

Since we are using 'YOLO V5s model', we set the default for weights as 'yolov5s'.
Image size is set = 608
conf-thres value = 0.3
iou-thres value = 0.4
view-img's action is set as 'store_false' to view the image.
save-txt's action is set as 'store_false' to save the result.
classes's default is added as 2 to only view car class.
The bounding box's line-thickness is edited to 2.
parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
parser.add_argument('--img-size', type=int, default=608, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.4, help='IOU threshold for NMS')
parser.add_argument('--view-img', action='store_false', help='display results')
parser.add_argument('--save-txt', action='store_false', help='save results to *.txt')
parser.add_argument('--classes', nargs='+', type=int, default=2, help='filter by class: --class 0, or --class 0 2 3')
parser.add_argument('--line-thickness', default=2, type=int, help='bounding box thickness (pixels)')

Demo Video

How to run the program

Download video to your yolov5 repository.
Download image to your yolov5\data\images directory.
Overwrite detect.py to the yolov5 repository.
Overwrite datasets.py to your yolov5\utils directory.

Future work

Detect the parking lines without conducting parking space image and adjusting the lines.
Specify the vehicles whether they are entering, leaving or staying for a minute.
Give an alarm to the securitary, if there is a double-parked car.

Reference

dudesparsh: Parking detector

PreviousMask Detection using YOLOv5 NextVehicle, Pedestrian Detection with IR Image

Last updated 2 years ago