## 4 Results and Discussion
### Verification of the Proposed Method  
The above evaluation quantitatively demonstrated the performance of the deviation monitoring network under different improvement strategies from an objective level, proving that the improved network model described in this paper performs well on the dataset. In this section, the effects of the proposed network are shown more intuitively from a subjective point of view. A part of the image data of the test set is assigned priority to be used in the generalization ability test of the network model (Yolov5-s). The respective influences of different conveying materials and their shapes, the image brightness and darkness, random noise, etc., on the detection results are discussed. The deep Hough transform (DHT) [33] linear detection algorithm is also used to compare the results. 1. The influence of the material flow  
Some types of conveying materials are similar to the conveyor belt in color, appearance, and form, especially a coal conveyor under the condition of low illumination. The presence of certain materials may interfere with the identification and detection results of the belt edge and may lead to the incorrect judgment of the deviation state. Figure 9 compares the detection effects of the method proposed in this paper and the DHT algorithm when different materials are transported and where the deviation threshold \(\tau\) is set as 80. Figure 9a is the original image used for network testing, collected from the power plant and the port. Figure 9b shows the DHT algorithm's detection effect on straight conveyor belt edge lines. It can be seen that the algorithm detects the belt edge and the boundary of the material flow and, in addition, the frame edge, indicating that the shape of the material flow and other potential straight lines indeed affect the belt edge detection to a certain extent. Figure 9c shows the detection results of the method proposed in this paper. Compared with the DHT algorithm, the proposed method can not only detect the left and right belt edges but also effectively avoids the influence of other potential lines in the field of vision on the detection results. Further, it provides the corresponding deviation amount and the judgment results of the deviation state. For example, \"offtrack:302.734\" means that the state of the conveyor belt is \"off-track\", and the deviation amount is  
Figure 9: Influence of conveying material flow on conveyor belt edge detection: (**a**) original image, (**b**) detection result of DHT, (**c**) detection result of the method proposed in this paper. 302.734 pixels, which is calculated according to Equations (8) and (9). The deviation state is judged according to the comparison between the calculated amount of deviation and the threshold value in Equation (6). Similarly, \"no_offtrack:42.466\" means that the conveyor belt's calculated deviation amount is 42.466 pixels, and the deviation state is determined to be \"no_offtrack\". In addition, there are still two values in the image shown in Figure 9c (\"\(-\)1.6195\" and \"1.5164\"), which are the slope of the identified conveyor belt edge line. When the camera is installed in line with the width of the conveyor belt, this value can be directly used in the auxiliary judgment of the belt deviation state, especially the belt inclined state. 2. The effect of the brightness and shade of the image In the open environment, at different periods, due to uneven illumination, the images obtained by the visual perception system of the belt conveyor may have different brightness degrees. Especially in the case of low illumination, the difference between the material flow and the conveyor belt will be less obvious, which easily leads to the monitoring method's failure. Therefore, in this section, we detail our simulation of conditions of low illumination. This was achieved by reducing the brightness of the image shown in Figure 9a to further verify the network's generalization ability.