Animation Character Detection Algorithm Based on Clustering and Cascaded SSD

With the evolution of the Internet and information technology, the era of big data is a new digital one. Accordingly, animation IP has been more and more widely welcomed and concerned with the continuous development of the domestic and international animation industry. Hence, animation video analysis will be a good landing application for computers. This paper proposes an algorithm based on clustering and cascaded SSD for object detection of animation characters in the big data environment. In the training process, the improved classification Loss function based on Focal Loss and Truncated Gradient was used to enhance the initial detection effect. In the detection phase, this algorithm designs a small target enhanced detection module cascaded with an SSD network. In this way, the high-level features corresponding to the small target region can be extracted separately to detect small targets, which can effectively enhance the detection effect of small targets. In order to further improve the effect of small target detection, the regional candidate box is reconstructed by a k-means clustering algorithm to improve the detection accuracy of the algorithm. Experimental results demonstrate that this method can effectively detect animation characters, and performance indicators are better than other existing algorithms.

Enhancing Intelligent Anemia Detection via Unifying Global and Local Views of Conjunctiva Image with Two-Branch Neural Networks

Anemia is one of the most widespread clinical symptoms all over the world, which could bring adverse effects on people's daily life and work. Considering the universality of anemia detection and the inconvenience of traditional blood testing methods, many deep learning detection methods based on image recognition have been developed in recent years, including the methods of anemia detection with individuals’ images of conjunctiva. However, existing methods using one single conjunctiva image could not reach comparable accuracy in anemia detection in many real-world application scenarios. To enhance intelligent anemia detection using conjunctiva images, we proposed a new algorithmic framework which could make full use of the data information contained in the image. To be concrete, we proposed to fully explore the global and local information in the image, and adopted a two-branch neural network architecture to unify the information of these two aspects. Compared with the existing methods, our method can fully explore the information contained in a single conjunctiva image and achieve more reliable anemia detection effect. Compared with other existing methods, the experimental results verified the effectiveness of the new algorithm.

Detection of DDoS Attacks in Software Defined Networking Using Entropy

Software Defined Networking (SDN) is one of the most commonly used network architectures in recent years. With the substantial increase in the number of Internet users, network security threats appear more frequently, which brings more concerns to SDN. Distributed denial of Service (DDoS) attacks are one of the most dangerous and frequent attacks in software defined networks. The traditional attack detection method using entropy has some defects such as slow attack detection and poor detection effect. In order to solve this problem, this paper proposed a method of fusion entropy, which detects attacks by measuring the randomness of network events. This method has the advantages of fast attack detection speed and obvious decrease in entropy value. The complementarity of information entropy and log energy entropy is effectively utilized. The experimental results show that the entropy value of the attack scenarios 91.25% lower than normal scenarios, which has greater advantages and significance compared with other attack detection methods.

Research on Internal Solitary Wave Detection and Analysis Based on Interferometric Imaging Radar Altimeter Onboard the Tiangong-2 Space Laboratory

The Tiangong-2 space laboratory was launched by China on 15 September 2016, carrying the Interferometric Imaging Radar Altimeter (InIRA), the first of the latest generation of imaging altimeters that can perform imaging and acquire elevation information simultaneously. This paper analyzes the feasibility of using InIRA images to obtain two-dimensional characteristics of oceanic internal solitary waves (ISWs) and information about vertical sea surface fluctuations caused by the propagation of ISWs. The results show that InIRA demonstrates a relatively reliable ability to observe ISWs with high resolution and can identify the fine-scale features of ISWs of different forms. Furthermore, InIRA can observe centimeter-level changes in the Sea Surface Height Anomaly (SSHA) caused by ISWs. The geometric relationship between the sensor’s flight direction and the propagation direction of ISWs does not affect its detection effect. However, the swath width of InIRA is too narrow to fully capture ISW information, and the height accuracy of InIRA height product images is not insufficient to detect the height information of small-scale ISWs. These shortcomings need to be considered in the future development of imaging altimeters to increase their potential for detecting mesoscale phenomena in the ocean.

Research on Key Security Detection Method of Cross Domain Information Sharing Based on PKG Trust Gateway

Aiming at the problems of low precision, long detection time and poor detection effect in current cross domain information sharing key security detection methods, a cross domain information sharing key security detection method based on PKG trust gateway is proposed. By analyzing bilinear pairing based on elliptic curve and identity based encryption scheme, according to the independent system parameters of PKG management platform, cross domain authentication access mechanism is proposed. PKG of different trust domains is used as the trust gateway for cross domain authentication. The key escrow problem of PKG of different trust domains is solved through key sharing, and the communication key agreement mechanism is established to mutually authenticate the user nodes in the trust domains with different system parameters. The formal description of the rule detection of cryptographic functions, parameters and other information, supported by the dynamic binary analysis platform pin, dynamically records the encryption and decryption process information during the operation of the program, and realizes cross domain information sharing key security detection through the design of correlation vulnerability detection algorithm. The experimental results show that the cross-domain information shared key security detection effect of the proposed method is better, which can effectively improve the detection accuracy and shorten the detection time.

A Target Detection Algorithm for Remote Sensing Images Based on Deep Learning

In order to improve the accuracy of remote sensing image target detection, this paper proposes a remote sensing image target detection algorithm DFS based on deep learning. Firstly, dimension clustering module, loss function, and sliding window segmentation detection are designed. The data set used in the experiment comes from GoogleEarth, and there are 6 types of objects: airplanes, boats, warehouses, large ships, bridges, and ports. Training set, verification set, and test set contain 73490 images, 22722 images, and 2138 images, respectively. It is assumed that the number of detected positive samples and negative samples is A and B, respectively, and the number of undetected positive samples and negative samples is C and D, respectively. The experimental results show that the precision-recall curve of DFS for six types of targets shows that DFS has the best detection effect for bridges and the worst detection effect for boats. The main reason is that the size of the bridge is relatively large, and it is clearly distinguished from the background in the image, so the detection difficulty is low. However, the target of the boat is very small, and it is easy to be mixed with the background, so it is difficult to detect. The MAP of DFS is improved by 12.82%, the detection accuracy is improved by 13%, and the recall rate is slightly decreased by 1% compared with YOLOv2. According to the number of detection targets, the number of false positives (FPs) of DFS is much less than that of YOLOv2. The false positive rate is greatly reduced. In addition, the average IOU of DFS is 11.84% higher than that of YOLOv2. For small target detection efficiency and large remote sensing image detection, the DFS algorithm has obvious advantages.

Water surface object detection using panoramic vision based on improved single-shot multibox detector

In view of the deficiencies in traditional visual water surface object detection, such as the existence of non-detection zones, failure to acquire global information, and deficiencies in a single-shot multibox detector (SSD) object detection algorithm such as remote detection and low detection precision of small objects, this study proposes a water surface object detection algorithm from panoramic vision based on an improved SSD. We reconstruct the backbone network for the SSD algorithm, replace VVG16 with a ResNet-50 network, and add five layers of feature extraction. More abundant semantic information of the shallow feature graph is obtained through a feature pyramid network structure with deconvolution. An experiment is conducted by building a water surface object dataset. Results showed the mean Average Precision (mAP) of the improved algorithm are increased by 4.03%, compared with the existing SSD detecting Algorithm. Improved algorithm can effectively improve the overall detection precision of water surface objects and enhance the detection effect of remote objects.

Detecting Insider Threat from Behavioral Logs Based on Ensemble and Self-Supervised Learning

Recent studies have highlighted that insider threats are more destructive than external network threats. Despite many research studies on this, the spatial heterogeneity and sample imbalance of input features still limit the effectiveness of existing machine learning-based detection methods. To solve this problem, we proposed a supervised insider threat detection method based on ensemble learning and self-supervised learning. Moreover, we propose an entity representation method based on TF-IDF to improve the detection effect. Experimental results show that the proposed method can effectively detect malicious sessions in CERT4.2 and CERT6.2 datasets, where the AUCs are 99.2% and 95.3% in the best case.

SwinGD: A Robust Grape Bunch Detection Model Based on Swin Transformer in Complex Vineyard Environment

Accurate recognition of fruits in the orchard is an important step for robot picking in the natural environment, since many CNN models have a low recognition rate when dealing with irregularly shaped and very dense fruits, such as a grape bunch. It is a new trend to use a transformer structure and apply it to a computer vision domain for image processing. This paper provides Swin Transformer and DETR models to achieve grape bunch detection. Additionally, they are compared with traditional CNN models, such as Faster-RCNN, SSD, and YOLO. In addition, the optimal number of stages for a Swin Transformer through experiments is selected. Furthermore, the latest YOLOX model is also used to make a comparison with the Swin Transformer, and the experimental results show that YOLOX has higher accuracy and better detection effect. The above models are trained under red grape datasets collected under natural light. In addition, the dataset is expanded through image data augmentation to achieve a better training effect. After 200 epochs of training, SwinGD obtained an exciting mAP value of 94% when IoU = 0.5. In case of overexposure, overdarkness, and occlusion, SwinGD can recognize more accurately and robustly compared with other models. At the same time, SwinGD still has a better effect when dealing with dense grape bunches. Furthermore, 100 pictures of grapes containing 655 grape bunches are downloaded from Baidu pictures to detect the effect. The Swin Transformer has an accuracy of 91.5%. In order to verify the universality of SwinGD, we conducted a test under green grape images. The experimental results show that SwinGD has a good effect in practical application. The success of SwinGD provides a new solution for precision harvesting in agriculture.