No abstract

In recent years, as one of the important technical tasks in the field of deep learning, object detection has broad prospects and applications in the field of road obstacle detection. However, in the real driving scene, there are many obstacles, serious occlusion, overlap and other problems, so that the existing obstacle detection algorithm can not effectively detect the obstacles on the road, so it can not guarantee the driving safety. In order to solve the above problems, this paper improves on the basis of Yolo V4 algorithm. Firstly, kmeans + + clustering is used to generate a priori box suitable for the data set to enhance the scale adaptability; Then, the ciou is used as the loss function of coordinate prediction to evaluate the coincidence degree of prediction frame and truth value frame more reasonably. Finally, a suitable target detection data set is constructed by preprocessing the public data set cityccaps. The experimental results show that the improved algorithm can achieve more than 90% accuracy for obstacles with large number of targets in the training set. Compared with the original Yolo V4, the average detection accuracy of the improved algorithm is improved by 2.03%.

In this paper, there is no unified grading standard for the harm of terrorist attacks. A classification model of terrorist incidents based on machine learning is proposed. First, the data related to the hazard in the Global Terrorism Database (GTD) is extracted and preprocessed. Secondly, the data is extracted by principal component analysis, and all events are aggregated into 5 by K-means clustering. Again, the entropy method is used to calculate the weighting coefficient of each indicator, and the comprehensive score of the hazard of each type of terrorist attack is calculated. Finally, the scores are divided into 1-5 levels of hazard grading models in order of high to low. The results show that the hazard grading model can scientifically and objectively quantify terrorist attacks.

Aiming at the distortion of texture details in Digital Camouflage design, as well as the poor camouflage performance, fast fading and short life of camouflage tent cloth, this paper presents a design method of Digital Camouflage based on target background, develops a camouflage coating with good weather resistance, color difference and spectral reflectance meeting the limited requirements, and realizes the paint printing of camouflage tent cloth. The results show that the digital camouflage pattern designed by this method can be printed on the surface of camouflage tent cloth by coating printing process, which can effectively change the original contour of the target tent, make it better integrate with the surrounding background, reduce the probability of detection, and achieve good camouflage effect.