## 4 Conclusions  
In order to improve the detection accuracy of an algorithm in the complex environment of a coal mine, including low illumination, motion blur, occlusions, small targets, and background interference; reduce the number of model parameters; improve the detection speed of the algorithm; and cause it to meet the needs of the real-time detection of edge equipment, our research was conducted from two angles: motion-blur image processing and the improved detection algorithm. The blur value of the image was calculated by the Laplace operator to accurately judge whether the image needed to be deblurred or not, and the blurred image was deblurred using DeblurGANv2. With YOLOv4 as the infrastructure, we made the following improvements: (1) replaced the original CSPDarknet53 network with the lightweight feature extraction network MobileNetv2; (2) embedded the SA attention module in the Neck network; (3) used the K-means++ algorithm to cluster the prior frames, and introduced the focal loss function to replace the cross-entropy loss function. The experimental results show that the deblurring of the motion-blurred image can effectively improve the detection accuracy of obstacles and reduce the number of missed detections. Compared with other commonly used models, the improved YOLOv4 algorithm has better results and can ensure the high-accuracy detection and speed of the model simultaneously. In this paper, experiments were conducted on our datasets, and the algorithm presented some limitations. The subsequent step is to increase the diversity and complexity of the data and further improve the network structure to improve the target detection accuracy under complex working conditions. The development of multi-sensor information fusion technology will be the focus of future research. This paper mainly studied the perception algorithm based on visual information, and the collection of the visual information was affected by shooting angles for the videos, a low-illumination environment, obstacle occlusions, rapid movements, and so on. Multi-sensor information fusion can make up for the shortcomings of a single sensor and realize the complementary advantages of multi-source information. At present, the domestic research concerning this technology is still in the initial stage, and the data fusion \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Average Precision (AP)/\%**} & \multirow{2}{*}{**mAP/\%**} & \multirow{2}{*}{**FPS**} \\ \cline{2-2} \cline{5-6}  & **E-L** & & & & \\ \hline Faster R-CNN [19] & 99.48 & 92.53 & 91.24 & 94.42 & 10 \\ YOLOv3 [13] & 99.87 & 98.84 & 90.78 & 96.50 & 39 \\ YOLOv3-tiny [41] & 98.43 & 98.46 & 81.83 & 92.91 & 104 \\ YOLOv4 [15] & 98.51 & 97.67 & 95.83 & 97.34 & 41 \\ YOLOv4-tiny [42] & 98.49 & 97.61 & 85.48 & 93.86 & 109 \\ Improved YOLOv4 & 99.16 & 98.65 & 96.24 & 98.02 & 68 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative experimental results for different target detection algorithms. method and implementation of the fusion system are key issues for the research conducted in the future. [PERSON] conceived the idea. [PERSON] and [PERSON] performed the data analyses and wrote the manuscript. [PERSON] and [PERSON] edited the manuscript. All authors discussed the results and revised the manuscript. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the Anhui Province University Outstanding Youth Research Project under Grant No. 2022 AH020056, the National Natural Science Foundation of China under Grant No. 52274152, the Collaborative Innovation Project of Universities in Anhui Province under Grant No. GXXT-2020-60, and the Graduate Innovation Fund of Anhui University of Science and Technology under Grant No. 2021 CX1008. Not applicable. The datasets generated and/or analyzed during the present study are available from the corresponding author on reasonable request.