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Pine wilt disease is extremely ruinous to forests. It is an important to hold back the transmission of the disease in order to detect diseased trees on UAV imagery, by using a detection algorithm. However, most of the existing detection algorithms for diseased trees ignore the interference of complex backgrounds to the diseased tree feature extraction in drone images. Moreover, the sampling range of the positive sample does not match the circular shape of the diseased tree in the existing sampling methods, resulting in a poor-quality positive sample of the sampled diseased tree. This paper proposes a Global Multi-Scale Channel Adaptation Network to solve these problems. Specifically, a global multi-scale channel attention module is developed, which alleviates the negative impact of background regions on the model. In addition, a center circle sampling method is proposed to make the sampling range of the positive sample fit the shape of a circular disease tree target, enhancing the positive sample’s sampling quality significantly. The experimental results show that our algorithm exceeds the seven mainstream algorithms on the diseased tree dataset, and achieves the best detection effect. The average precision (AP) and the recall are 79.8% and 86.6%, respectively.

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Topology partition imposes significant challenge on reliable delivery of warning messages to vehicles in the zone of relevance (ZOR) in Vehicular Ad Hoc Networks (VANETs). In order to improve the probability of delivering warning messages to all vehicles in the ZOR, VANETs rely on periodic broadcast to disseminate warning messages, which brings the problem of how to set proper message lifetime for the warning message. In this paper, in order to analyze this warning message propagation process and suggest a proper message lifetime for the V2V network, we first propose analytical models to study the warning message propagation process in the connected and partitioned network. Then, based on the proposed models, we derive the delivery probability under different traffic conditions. Finally, through numerical results, we evaluate the impact of traffic characteristics on the setting of the message lifetime. Our results show that the warning message propagation process depends on some vehicle traffic characteristics, e.g., vehicle density, speed and dangerous time. Our results also show that when the message lifetime increases to certain values under different traffic conditions, it will not increase the delivery reliability, but only cause serious redundancy.