Network For Real-time Detection Research Articles

A novel approach for credit card fraud transaction detection using deep reinforcement learning scheme.

Online transactions are still the backbone of the financial industry worldwide today. Millions of consumers use credit cards for their daily transactions, which has led to an exponential rise in credit card fraud. Over time, many variations and schemes of fraudulent transactions have been reported. Nevertheless, it remains a difficult task to detect credit card fraud in real-time. It can be assumed that each person has a unique transaction pattern that may change over time. The work in this article aims to (1) understand how deep reinforcement learning can play an important role in detecting credit card fraud with changing human patterns, and (2) develop a solution architecture for real-time fraud detection. Our proposed model utilizes the Deep Q network for real-time detection. The Kaggle dataset available online was used to train and test the model. As a result, a validation performance of 97.10% was achieved with the proposed deep learning component. In addition, the reinforcement learning component has a learning rate of 80%. The proposed model was able to learn patterns autonomously based on previous events. It adapts to the pattern changes over time and can take them into account without further manual training.

$$\hbox {ABDF}^{2}$$-Net: an adaptive bi-directional features fusion network for real-time detection of threat object

$$\hbox {ABDF}^{2}$$-Net: an adaptive bi-directional features fusion network for real-time detection of threat object

A Real-Time Steel Surface Defect Detection Approach With High Accuracy

Surface defect inspection is a key step to ensure the quality of the hot rolled steel surface. However, current advanced detection (DET) methods have high precision but low detection speed, which hinders the application of the detector in actual production. In this work, a real-time detection network (RDN) focusing on both speed and accuracy is proposed to solve the problem of steel surface defect detection. RDN takes ResNet-dcn, a modular encoding, and decoding network with light weights, as the basic convolutional architecture whose backbone is pretrained on ImageNet. To improve the detection accuracy, a skip layer connection module (SCM) and a pyramid feature fusion module (PFM) are involved into RDN. On the standard dataset NEU-DET, the proposed method can achieve the state-of-the-art recognition speed of 64 frames per second (FPS) and the mean average precision of 80.0% on a single GPU, which fully meets the requirements of the detection accuracy and speed in the actual production line.

Real-Time Obstacle Detection Based on Image Semantic Segmentation and Fusion Network

During fruit production, the robots must walk stably across the orchard, and detect the obstacles in real time on its path. With the rapid process of deep convolutional neural network (CNN), it is now a hot topic to enable orchard robots to detect obstacles through image semantic segmentation. However, most such obstacle detection schemes are under performing in the complex environment of orchards. To solve the problem, this paper proposes an image semantic fusion network for real-time detection of small obstacles. Two branches were set up to extract features from red-green-blue (RGB) image and depth image, respectively. The information extracted by different modules were merged to complement the image features. The proposed network can operate rapidly, and support the real-time detection of obstacles by orchard robots. Experiments on orchard scenarios show that our network is superior to the latest image semantic segmentation methods, highly accurate in the recognition of high-definition images, and extremely fast in reasoning.

Real-Time Detection and Motion Recognition of Human Moving Objects Based on Deep Learning and Multi-Scale Feature Fusion in Video

At present, human body moving target detection and recognition algorithms based on deep learning have made breakthrough progress. However, in some applications with high real-time requirements, the existing deep learning real-time detection and recognition network is difficult to achieve high detection accuracy. Therefore, how to achieve accurate positioning and recognition of human moving targets while ensuring real-time detection is still an urgent problem in this field. Based on the single shot multi-box detector (SSD) real-time detection network, this paper proposes a real-time detection positioning and recognition network based on multi-scale feature fusion (IMFF-SSD), which improves the positioning accuracy and identification accuracy. First, this article analyzes the multi-scale features extracted from the SSD network. It combines the position-sensitive information provided by low-level detail features with the context information provided by high-level semantic features through feature fusion, which effectively improves positioning accuracy of the target prediction layer in the SSD network. Secondly, a feature embedded prediction structure is designed to strengthen the semantics of target features without changing the spatial resolution of the SSD prediction layer, and embed low-scale detailed features in high-semantic features for collaborative prediction of targets. This improves the accuracy of the SSD network's recognition of human moving targets at all scales. The experimental results show that by combining the above two improvements, the real-time monitoring and recognition network based on multi-scale feature fusion proposed in this paper has achieved a greater degree of improvement in positioning accuracy and motion recognition accuracy than the original SSD, which is better than some current the human body moving object detection and recognition algorithm has great advantages.

ZigBee Technology Application in Disaster Rescue

In the scene of disaster rescue, there are usually many potential hazards. How to guarantee the safety of the Rescue Personnel and the rescue efficiency is a problem that deserves our concern. From the perspective of rescue security, this paper explores a new program that adopts ZigBee technology in disaster rescue. By making use of ZigBee technology's features of high-reliability and low-complexity, it can make the gas acquisition module, image acquisition module and human detection module to form a wireless sensing probe network and then realize the environmental monitoring of disaster site and disaster victims searching and rescuing. In this way, it can overcome its previous shortcomings, such as poor real-time detection network, difficulty in wiring, etc. and thus to achieve the purpose of efficient early warning and rescue.