Aiming at the problems of low optimization accuracy and slow convergence of Simple Human Learning Optimization （SHLO） algorithm， a new Human Learning Optimization algorithm based on Learning Psychology （LPHLO） was proposed. Firstly， based on Team-Based Learning （TBL） theory in learning psychology， the TBL operator was introduced， so that on the basis of individual experience and social experience， team experience was added to control individual learning state to avoid the premature convergence of algorithm. Then， the memory coding theory was combined to propose the dynamic parameter adjustment strategy， thereby effectively integrating the individual information， social information and team information. And the abilities of the algorithm to explore locally and develop globally were better balanced. Two examples of knapsack problem of typical combinatorial optimization problems， 0-1 knapsack problem and multi-constraint knapsack problem， were selected for simulation experiments. Experimental results show that， compared with the algorithms such as SHLO algorithm， Genetic Algorithm （GA） and Binary Particle Swarm Optimization （BPSO） algorithm， the proposed LPHLO has more advantages in optimization accuracy and convergence speed， and has a better ability to solve the practical problems.

In order to solve the problems of small targets， large noises， and many types in the logo recognition for vehicles on traffic road， a method combining a target detection algorithm based on deep learning and a template matching algorithm based on morphology was proposed， and a recognition system with high accuracy and capable of dealing with new types of vehicle logo was designed. First， K-Means++ was used to re-cluster the anchor box values and residual network was introduced into YOLOv4 for one-step positioning of the vehicle logo. Secondly， the binary vehicle logo template library was built by preprocessing and segmenting standard vehicle logo images. Then， the positioned vehicle logo was preprocessed by MSRCR （Multi-Scale Retinex with Color Restoration）， OTSU binarization， etc. Finally， the Hamming distance was calculated between the processed vehicle logo and the standard vehicle logo in the template library and the best match was found. In the vehicle logo detection experiment， the improved YOLOv4 detection achieves the higher accuracy of 99.04% compared to the original YOLOv4， two-stage positioning method of vehicle logo based on license plate position and the vehicle logo positioning method based on radiator grid background； its speed is slightly lower than that of the original YOLOv4， higher than those of the other two， reaching 50.62 fps （frames per second）. In the vehicle logo recognition experiment， the recognition accuracy based on morphological template matching is higher compared to traditional Histogram Of Oriented Gradients （HOG）， Local Binary Pattern （LBP） and convolutional neural network， reaching 91.04%. Experimental results show that the vehicle logo detection algorithm based on deep learning has higher accuracy and faster speed. The morphological template matching method can maintain a high recognition accuracy under the conditions of light change and noise pollution.

In order to reduce the redundant connections and unnecessary computing cost, quantum-inspired clonal algorithm was applied to optimize neural networks. By generating neural network weights which have certain sparse ratio, the algorithm not only effectively removed redundant neural network connections and hidden layer nodes, but also improved the learning efficiency of neural network, the approximation of function accuracy and generalization ability. This method had been applied to wild relics security system of Emperor Qinshihuang's mausoleum site museum, and the results show that the method can raise the probability of target classification and reduce the false alarm rate.