이러한 선의 각도를 알고있는 매우 낮은 품질의 이미지에서 선을 찾는 가장 좋은 방법은 무엇입니까?

the issue is the noise and the faint signal. you can subdue the noise with averaging/integration, while maintaining the signal because it's replicated along a dimension (signal is a line).

your approach using a very wide but narrow kernel can be extended to simply integrating along the whole image.

1. rotate the image so the suspected line is aligned with an axis (let's say horizontal)
2. sum up all pixels of one scanline (horizontal line), np.sum(axis=1) or mean, either way mind the data type. working with floats is convenient.
3. work with the 1-dimensional series of values.

this will not tell you how long the line is, only that it's there and potentially spanning the whole width.

edit: since my answer got a reaction, I'll elaborate as well:

나는 당신이 "회색"기준선을 얻기 위해 그것을 로우 패스 할 수 있다고 생각한다. 그리고 나서 빼기 ( "가우스의 차이"). 그것은 당신에게 좋은 신호를 줄 것입니다.

import numpy as np
import cv2 as cv
import matplotlib.pyplot as plt
import scipy.ndimage

im = cv.imread("0gczo.png", cv.IMREAD_GRAYSCALE) / np.float32(255)
relief = im.mean(axis=1)
smoothed = scipy.ndimage.gaussian_filter(relief, sigma=2.0)
baseline = scipy.ndimage.gaussian_filter(relief, sigma=10.0)
difference = smoothed - baseline
std = np.std(difference)
level = 2
outliers = (difference <= std * -level)
plt.plot(difference)
plt.hlines([std * +level, std * -level], xmin=0, xmax=len(relief))
plt.plot(std * -level + outliers * std)
plt.show()
# where those peaks are:
edgemap = np.diff(outliers.astype(np.int8))
(edges,) = edgemap.nonzero()
print(edges)           # [392 398 421 427]
print(edgemap[edges])  # [ 1 -1  1 -1]