In view of the complex underground structure and harsh environment of coal mines, it is easy to cause problems such as low pedestrian detection accuracy and missed detection. An improved mine pedestrian detection algorithm based on YOLOv4-Tiny was proposed. The algorithm introduced spatial pyramid pooling (SPP) module after 13×13 features, which realized the extraction of global and local features of image information, and finally improved the detection precision of the system. The mAP of the improved algorithm was 91.98%, which was 2.32% higher than the original algorithm, the precision increased by 3.87% and the recall by 0.93%. The experimental results showed that the algorithm improved the detection of the mine pedestrian system effectively and had a better detection effect.
Compared with a single drone, the bee swarm unmanned aerial vehicles(UAVs) have a higher fault tolerance and mission accomplished rate. However, When the UAVs under a complex environment with electromagnetic interference, the communication connections will be interfered greatly, so the position information exchanging unable to work normally. The ultraviolet communication in this article has the advantages of non-line-of-sight, all-weather, and strong antiinterference ability. The structure of the ultraviolet beacon is designed to improve the communication quality and positioning capabilities of the bee swarm UAVs in electromagnetic interference. By building a flight guidance tracking error model and improving the performance of leader-follower algorithm, it is possible to solve the issue of large error feedback during the assembly of bee swarm UAVs. Finally, through simulation experiments to verify the accuracy of the above analysis.
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