What type of neural network should I use to detect meteors in images? I am currently building a project that takes fisheye images from cameras and detects whether the picture contains a meteor, and if it does it tries to identify where the meteor is.

The images look like this: 

I am currently building a project that takes fisheye images from cameras and detects whether the picture contains a meteor.

The images look like this: 

The 'circle' in the middle is the actual sky, everything outside it is just black pixels

The meteor is the long flash of light in the middle, the small flashes of light on the 'sides' are lights that come from the cities nearby.

Using opencv's HoughLines I was able to create a program that detects the meteors, however it also detects the lights on the 'sides', so my idea was to train a neural network to only detect the meteors.

How would I go about doing this? I was thinking of using a U-Net neural network, using the images and masks of the images where only the meteor is 'detected' for training, sort of like this example.
This is not a NN solution but can you adjust the parameters to opencv's HoughLine? There should be a way to find just the straight lines. There should be a way to adjust the minimum line length value to be considered a meteors. Meteors will always appear straight. So only keep the results when the points have similar slopes and are near each other?

See this example:

import cv2
import numpy as np

img = cv2.imread('dave.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray,50,150,apertureSize = 3)
minLineLength = 100
maxLineGap = 10
lines = cv2.HoughLinesP(edges,1,np.pi/180,100,minLineLength,maxLineGap)
for x1,y1,x2,y2 in lines[0]:
    cv2.line(img,(x1,y1),(x2,y2),(0,255,0),2)

cv2.imwrite('houghlines5.jpg',img)


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