In traffic accidents, motorcycle accidents are the main cause of casualties, especially in developing countries. The main cause of fatal injuries in motorcycle accidents is that motorcycle riders or passengers do not wear helmets. In this paper, an automatic helmet detection of motorcyclists method based on deep learning is presented. The method consists of two steps. The first step uses the improved YOLOv5 detector to detect motorcycles (including motorcyclists) from video surveillance. The second step takes the motorcycles detected in the previous step as input and continues to use the improved YOLOv5 detector to detect whether the motorcyclists wear helmets. The improvement of the YOLOv5 detector includes the fusion of triplet attention and the use of soft-NMS instead of NMS. A new motorcycle helmet dataset (HFUT-MH) is being proposed, which is larger and more comprehensive than the existing dataset derived from multiple traffic monitoring in Chinese cities. Finally, the proposed method is verified by experiments and compared with other state-of-the-art methods. Our method achieves mAP of 97.7%, F1-score of 92.7% and frames per second (FPS) of 63, which outperforms other state-of-the-art detection methods.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

The Cognitive Radio (CR) can take full advantage of the spectrum by enabling second user (SU) to access idle radio channel licensed by primary user (PU) to improve spectrum utilization. The Orthogonal Frequency Division Multiplexing (OFDM) provides a feasible network air interface scheme for CR with the flexibility of distributing power and bit to achieve tremendous data rate with the least power. And the mutual interference is a limiting factor that should be taken into account due to the non-orthogonality of their transmit signals. And in this paper we analyze the theoretical system capacity with available sub-channels, subject to interference tolerance and power ceiling appointed by PU in a downlink OFDM-Based CR multiuser scenario, and employ a Particle Swarm Optimization-Based (PSO) algorithm to achieve the resource allocation.