# A Millimeter-Wave Radar Tunnel Obstacle Detection Method Based on Invalid Target Filtering
## 4 Conclusions  
Taking millimeter-wave radar as a sensor, a method of filtering and dividing the data generated by millimeter-wave radar to reduce the amount of computation is proposed. After analyzing the target information output by millimeter-wave radar, the target obstacle information in the roadway is extracted, and the invalid target filtering algorithm is developed to detect, judge and filter the data of empty targets, false targets, and non-threat targets after analyzing, so as to improve the obstacle recognition ability of unmanned electric locomotives in coal mines. It provides a further contribution to the application of millimeter-wave radar in underground roadways of coal mines.  
Using the car as a platform, three states of tests were conducted: stationary, smooth straight line, and straight line with vibration. The test showed that the algorithm used in this paper could effectively filter out invalid targets, with an average filtering rate of over 87% for stationary state and over 86% for both smooth straight lines and straight lines with vibration.  
The average filtering effect of this algorithm is more than 86%. Regarding vision, the YOLOv4-Tiny-4S algorithm can accurately detect and identify all kinds of targets in the image, with an average detection accuracy of 95.35% and an average detection accuracy  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **First Time** & **Second** & **Third** & **Average Filtration Rate** \\ \hline Stationary & 87.72\% & 87.76\% & 87.97\% & 87.82\% \\ Smooth driving 5 km/h/(\%) & 87.74\% & 87.59\% & 87.76\% & 87.70\% \\ Smooth driving 7 km/h/(\%) & 86.62\% & 86.49\% & 87.39\% & 86.83\% \\ Smooth driving 10 km/h/(\%) & 86.56\% & 86.52\% & 86.54\% & 86.54\% \\ Driving with vibration 5 km/h/(\%) & 86.64\% & 86.59\% & 86.46\% & 86.56\% \\ Driving with vibration 7 km/h/(\%) & 86.62\% & 86.49\% & 86.39\% & 86.50\% \\ Driving with vibration 10 km/h/(\%) & 86.51\% & 86.32\% & 86.49\% & 86.44\% \\ \hline \end{tabular}
\end{table}
Table 6: Comparison of invalid target filtering rates.  
of 86.69% for small target \"gravel.\" However, the camera has the disadvantages of being greatly affected by light and dust and low reliability, and its real-time performance is limited by the computing ability of airborne equipment. The underground environment of a coal mine is dusty and dark, vision is greatly affected, and precision cannot be achieved. However, millimeter-wave radar and vision can complement each other well, so it is significant to improve the detection accuracy of millimeter-wave radar. It is hoped that in future work, the computing ability of the on-board equipment can be improved, the obstacle data set in underground coal mine can be perfected, the fusion model can be fully trained, the accuracy of obstacle detection can be improved, and the unmanned driving of electric locomotive in underground coal mine can be realized as soon as possible. Thus, improving the safety of coal mine production operations and providing economic and social benefits.  
Methodology, Y.P.; Software, F.H.; Validation, Z.W. and S.Z.; Formal analysis, Z.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Hebei Province (E2022402102). This support is greatly acknowledged and appreciated.  
The main data is presented in the text, and there is no new data.  
The authors declare that there is no conflict of interest regarding the publication of this article.