Aiming at the problem of low real-time performance of face recognition and low face recognition rate on ARM platform, a real-time face recognition method based on deep learning was proposed. Firstly, an algorithm for detecting and tracking faces in real time was designed based on MTCNN face detection algorithm. Then, a face feature extraction network was designed based on Residual Neural Network (ResNet) on ARM platform. Finally, according to the characteristics of ARM platform, Mali-GPU was used to accelerate the operation of face feature extraction network, sharing the CPU load and improving the overall running efficiency of the system. The algorithm was deployed on ARM-based Rockchip development board, and the running speed reaches 22 frames per second. Experimental results show that the recognition rate of this method is 11 percentage points higher than that of MobileFaceNet on MegaFace.

In order to solve the detection of ships with different types and sizes under complex marine environment, a real-time object detection algorithm based on deep learning was proposed. Firstly, a discriminant method between sharp and fuzzy such as rainy and foggy images was proposed. Then a multi-scale object detection algorithm based on deep learning framework of You Only Look Once (YOLO) v2 was proposed. Finally, concerning the character of remote sensing images of ships, an improved non-maximum supression and saliency partitioning algorithm was proposed to optimize the final detection results. The experimental results show that, on the dataset of ship detection in an open competition under complex sea conditions and meteorological conditions, the precision of the proposed method is increased by 16% compared with original YOLO v2 algorithm.

Aiming at the shortage that Particle Swarm Optimization (PSO) algorithm is easy to fall into local optima and has low precision at later evolution process, a modified Multi-Strategies synergy PSO (MSPSO) algorithm was proposed. Firstly, a probability threshold value of 0.3 was set. In every iteration, if the randomly generated probability value was less than the threshold, the algorithm with opposition-based learning for the best individual was adopted to generate their opposite solutions, which improved the convergence speed and precision of PSO; otherwise, Gaussian mutation strategy was adopted for the particle position to enhance the diversity of population. Secondly, a Cauchy mutation strategy for linearly decreasing cauchy distribution scale parameter decreased was proposed, to generate better solution to guide the particle to approximate the optimum space. Finally, the simulation experiments were conducted on eight benchmark functions. MSPSO algorithm has the convergence mean value of 1.68E+01, 2.36E-283, 8.88E-16, 2.78E-05, 8.88E-16, respectively in Rosenbrock, Schwefel's P2.22, Rotated Ackley, Quadric Noise and Ackley, and can converge to the optimal solution of 0 in Sphere, Griewank and Rastrigin, which is better than GDPSO (PSO based on Gaussian Disturbance) and GOPSO (PSO based on global best Cauchy mutation and Opposition-based learning). The results show that proposed algorithm has higher convergence accuracy and can effectively avoid being trapped in local optimal solution.