# Application of Lightweight Convolutional Neural Network for Damage Detection of Conveyor Belt
## 4 Conclusions and Future Work  
Aiming at the problem of conveyor belt damage detection, the paper proposed a detection method based on a lightweight neural network, which aims to increase the detection speed to meet the development needs of high-speed belt conveyors, also to match the cameras with high frame rate, making the signal processing speed more real-time.  
In this paper, the Mobilenet network and the Yolov4 target detection network are effectively combined to achieve the simplification of the Yolov4 network. Meanwhile, a series of different lightweight models are achieved by adjusting the number of channels, also achieved a good detection effect on the conveyor belt damage dataset, with a highest detection accuracy of 93.22% and a fastest detection speed of 70.26 FPS. Compared with Yolov4, the accuracy is increased by 3.5%, and the speed is increased by 188%.  
The contributions of this paper can be summarized as follows:  
1. A lightweight Yolov4 network is realized through the effective combination of MobileNet and Yolov4 network.
2. A series of lightweight networks with different degrees of lightness are obtained by adjusting the number of channels, and their influence on detection speed and detection accuracy is explored.
3. The application of lightweight neural networks in conveyor belt damage detection is further explored. The results show that lightweight neural networks can bring significant improvement in detection speed with certain loss of accuracy compared to the original neural networks, but when the number of channels of lightweight neural networks is further expanded, their backbone feature extraction capability is further enhanced and their prediction accuracy even catches up with the original Yolov4.
4. The generalization capability of the target detection model is further validated by comparing the conveyor belt damage data in the existing literature.  
Figure 8: Generalization proficiency verification: (**a,d,g,j,m,p**) are the original images, (**b,e,h,k,n,q**) belong to the detection results using method in this paper, (**c,f,i,l,o,r**) are the detection results given in Refs. [25; 26; 29; 30; 32].  
In the following research, in addition to further expanding the dataset and improving the detection accuracy, we will also attach importance to the potential effects of image collection conditions carefully, such as dust, light, etc.  
M.Z. ([PERSON]): Methodology, Writing--original draft, Software; Y.Z.: Supervision; M.Z. ([PERSON]): Conceptualization; K.J.: Network structure drawing and improvement; H.S.: Data curation; Y.Y.: Investigation, Supervision; N.H.: Conceptualization, Writing--review & editing. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the financial supports by State Key Laboratory of Mining Disaster Prevention and Control (MDFC2016 ZR05).  
The data in this study are available on request from the corresponding author.  
The authors wish to acknowledge [PERSON] at University of Exeter, United Kingdom and [PERSON], postgraduate of Shandong University of Science and Technology, China, for their help in collecting the original data and labeling.  
The authors declare no conflict of interests.