## 4 Results and Discussion
### Verification of the Proposed Method
The results are shown in Figure 10.  
Among them, Figure 10a shows the result of reducing the image brightness in Figure 9a, while Figure 10b shows the detection effect of the DHT algorithm. By comparing Figures 9b and 10b, it is found that the influence of the material flow on the detection results still exists under low illumination. The influence of other potential lines in the field of vision (such as the frame) on the detection results of the belt edge of the conveyor decreases gradually. Figure 10c shows the detection results of low-brightness images for the network model proposed in this paper. It can be seen that the method proposed in this paper is not  
Figure 10: Influence of low illumination on conveyor belt edge detection: (**a**) image with low illumination, (**b**) detection result of DHT, (**c**) detection result of the method proposed in this paper. affected by the image brightness and can still generate a correct judgment on the state of the conveyor belt deviation, but the calculated amount of deviation is slightly different from the result shown in Figure 9c. This difference is caused by the different detection results for the detected edge line. It can be seen from the comparison that the slopes of the belt edge detected by the network are different. Further, when the deviation amount is calculated according to Equations (8)-(10), different deviation amounts are obtained, and different deviation states may be determined. Thus, the threshold value should be set reasonably according to the site conditions to accurately determine the running state of the conveyor belt. Effects of random vibration and noise from rain and snow  
In the daily use of the camera, some unavoidable external forces (camera base vibration, strong wind, rain and snow, etc.) will inevitably lead to image dithering, blur, and random noise in the camera's field of vision. This will affect the detection results. In this section, global motion blur and global rain or snow noise are added to the original image shown in Figure 9a using image processing technology to simulate the interference of the camera shaking and of rainy or snowy weather, and the respective influence of these types of interference on the algorithm's detection results is explored in Figures 11 and 12. According to the influence of image dithering on the detection results shown in Figure 11, compared with the DHT algorithm, the proposed method in this paper still has better stability under the condition of camera (image) dithering and is not disturbed by other \"potential\" lines in the field of vision. However, by comparing the calculated value of the amount of deviation and the judgment results, it can be seen that, in this case, camera (image) dithering greatly interferes with the proposed method. This leads to the unreliability of the calculations regarding deviation and poor accuracy. By analyzing the  
Figure 11: Influence of image jitter on conveyor belt edge detection: (**a**) image with motion blur, (**b**) detection result of DHT, (**c**) detection result of the method proposed in this paper. influence of rain or snow noise on the detection results in Figure 12, it can be seen that rain and snow noise greatly impact both the DHT algorithm and the proposed method, which leads to both misses and false detection in the scenario of low-illumination power plants, but the method proposed in this paper still maintains a good detection effect in other scenarios.