Recently, object detection methods have developed rapidly and have been widely used in many areas. In many scenarios, helmet wearing detection is very useful, because people are required to wear helmets to protect their safety when they work in construction sites or cycle in the streets. However, for the problem of helmet wearing detection in complex scenes such as construction sites and workshops, the detection accuracy of current approaches still needs to be improved. In this work, we analyze the mechanism and performance of several detection algorithms and identify two feasible base algorithms that have complementary advantages. We use one base algorithm to detect relatively large heads and helmets. Also, we use the other base algorithm to detect relatively small heads, and we add another convolutional neural network to detect whether there is a helmet above each head. Then, we integrate these two base algorithms with an ensemble method. In this method, we first propose an approach to merge information of heads and helmets from the base algorithms, and then propose a linear function to estimate the confidence score of the identified heads and helmets. Experiments on a benchmark data set show that, our approach increases the precision and recall for base algorithms, and the mean Average Precision of our approach is 0.93, which is better than many other approaches. With GPU acceleration, our approach can achieve real-time processing on contemporary computers, which is useful in practice.

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The flexibility and convenience offered by mobile communication have made it one of the fastest growing areas of telecommunications, and communication security has become particularly significant and prominent. One of the most challenging security threats is the attack of rogue base station which posing as a radio transceiver station. It uses the same high-power electromagnetic frequencies to occupy the spectrum resources to send large amounts of spam messages. Rogue base station, if undetected, can be an open door to sensitive information. In this paper, we present the process which rogue base station exploits security vulnerabilities to attack. Based on the priority handover strategy of channel monitoring in cellular radio system and the SMS which Attention instruction commands MODEM to receive in PDU format, we propose a self-testing approach defending against rogue base station hijacking of intelligent terminal. Extensive experiments have demonstrated the accuracy, effectiveness, and robustness of our approach.

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