Aiming at the problem of low detection rate of depth feature based object detection method Faster R-CNN （Faster Region-based Convolutional Neural Network） in flame detection tasks, a color-guided anchoring strategy was proposed. In this strategy, a flame color model was designed to limit the generation of anchors, which means the flame color was used to limit the generation locations of the anchors, thereby reducing the number of initial anchors and improving the computational efficiency. To further improve the computational efficiency of the network, the masked convolution was used to replace the original convolution layer in the region proposal network. Experiments were conducted on BoWFire and Corsician datasets to verify the detection performance of the proposed method. The experimental results show that the proposed method improves detection speed by 10.1% compared to the original Faster R-CNN, has the F-measure of flame detection of 0.87 on BoWFire, and has the accuracy reached 99.33% on Corsician.The proposed method can improve the efficiency of flame detection and can accurately detect flames in images.

Due to random initialization of the weights, Deep Belief Network (DBN) easily falls into a local optimum, the Cuckoo Search (CS) algorithm was introduced into the traditional DBN model and a lung cancer image recognition algorithm based on CS-DBN was proposed. Firstly, the global optimization ability of CS was used to optimize initial weights of DBN, and on this basis, the layer-by-layer pre-training of DBN was performed. Secondly, the whole network was fine-tuned by using Back Propagation (BP) algorithm, so that the network weights were optimized. Finally, the CS-DBN was applied to the identification of lung tumor images, and CS-DBN was compared with traditional DBN from the four perspectives of Restricted Boltzmann Machine (RBM) training times, training batch sizes, DBN hidden layers numbers, and hidden layer nodes to verify the feasibility and effectiveness of the algorithm. The experimental results show that the recognition accuracy of CS-DBN is obviously higher than that of traditional DBN. Under the conditions of different RBM training times, training batch sizes, DBN hidden layer numbers, and hidden layer nodes, the increase range of CS-DBN identification accuracy over traditional DBN are 1.13 to 4.33, 2 to 3.34, 1.07 to 3.34 and 1.4 to 3.34 percentage points respectively. CS-DBN can improve the accuracy of lung tumor recognition to a certain extent, thereby improving the performance of computer-aided diagnosis of lung tumors.

Lower Energy Adaptive Clustering Hierarchy (LEACH) protocol randomly and circularly selects the cluster-head node and evenly distributes network energy consumption to each sensor node, but it does not consider the remaining energy of each node. In order to avoid premature death of the less energy node that was selected as the cluster-head node, an advanced algorithm named LEACH-New was proposed,which was based on the energy probability to select those nodes with more energy as cluster-head and to determine the optimal number of the cluster-head nodes. The cluster-head node collected, fused, then sent the data to the base station by the combined mode of single-hop and multi-hop. This algorithm resolved the problem that less energy node was selected to be cluster-head and cluster-heads energy overloaded in LEACH protocol, so it can prolong the lifetime of whole network. The simulation results show that the improved algorithm effectively reduces the network energy consumption and ensure network load balance.