Scene Recognition from Image Using Convolutional Neural Network

Masood, Sarfaraz; Ahsan, Umer; Munawwar, Fatima; Rizvi, Danish Raza; Ahmed, Mumtaz

doi:10.1016/j.procs.2020.03.400

Cited by 10 publications

(3 citation statements)

References 11 publications

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“…Literature [20] introduces the method from OpenCV, which improves the face recognition technique by using the sweet face method and the optimal map method, and at the same time, enhances the side face, occlusion and exaggerated expression detection and recognition to improve the accuracy. Literature [21], in order to solve the problem of scene recognition, explores the application ability of the dataset of PLACES2, which contains close to 7 million images of various scenes, which can be used as inputs for training and testing the model, and design the neural architecture of the machine through convolutional neural network, which is used to store the images required for scene recognition, and conduct simulation experiments after a large number of training to verify the reliability of the dataset. Literature [22] proposed a multi-feature fusion method based on weighted sequence fusion to obtain fused feature vectors of active and passive millimeter-wave urban and rural areas, which is applied with millimeterwave imaging target recognition, and the performance of this method is proved to be better than that based on the original feature vectors.…”

Section: Introductionmentioning

confidence: 99%

Research and realization of computer image recognition system based on digital projection technology

2024

Applied Mathematics and Nonlinear Sciences

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In this paper, the image Gabor features extracted by Gabor wavelet are fused with the image grayscale map to construct the enhanced Gabor features, and then combined with the characteristics of Gabor wavelet and convolutional layer, the Gabor feature extraction module, parallel convolution module and spatial transformation pooling module are designed. The corresponding Gabor convolutional layer and Gabor convolutional neural network are constructed using the appropriate module in accordance with the image recognition task application scenario. The convex set projection image super-resolution reconstruction method is used in this paper to improve the resolution of images with low resolution. The construction of a computerized image recognition system involves combining a Gabor convolutional neural network and a convex set projection method. This system has been tested and found to have a recognition accuracy of 93.5% for object images. This system’s ability to accurately recognize low-resolution shadow-obscured face images is possible thanks to using the convex set projection method to reconstruct the image and recognize it accurately with an accuracy of up to 93.85%. This system’s recognition performance for complex images has been proven through experiments.

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Section: Introductionmentioning

confidence: 99%

Research and realization of computer image recognition system based on digital projection technology

2024

Applied Mathematics and Nonlinear Sciences

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“…For example, a pixel in an image can be thought of as a vector of density values; pixels form a feature, where a feature is a cluster of custom shapes such as corners, edges, and roundness 5 . The simple design of the deep learning model is the convolutional neural network (CNN), which includes convolution filters, pooling, activation function, dropout, fully connected, and classification layers 6 . In addition, there are various methods for object detection using deep learning, including Faster R-CNN, You Only Look Once (YOLO), and Single Shot Detection (SSD).…”

Section: Introductionmentioning

confidence: 99%

Improving video surveillance systems in banks using deep learning techniques

Zahrawi

Shaalan

2023

Sci Rep

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In the contemporary world, security and safety are significant concerns for any country that wants to succeed in tourism, attracting investors, and economics. Manually, guards monitoring 24/7 for robberies or crimes becomes an exhaustive task, and real-time response is essential and helpful for preventing armed robberies at banks, casinos, houses, and ATMs. This paper presents a study based on real-time object detection systems for weapons auto-detection in video surveillance systems. We propose an early weapon detection framework using state-of-the-art, real-time object detection systems such as YOLO and SSD (Single Shot Multi-Box Detector). In addition, we considered closely reducing the number of false alarms in order to employ the model in real-life applications. The model is suitable for indoor surveillance cameras in banks, supermarkets, malls, gas stations, and so forth. The model can be employed as a precautionary system to prevent robberies by implying the model in outdoor surveillance cameras.

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“…The representation of an image can be considered to comprise a vector of density values per pixel or features such as clusters of edges and custom shapes, with some representing the data better [12]. The basic architecture of the deep learning concept is the convolutional neural network (CNN), which consists of convolution, pooling, activation function, dropout, fully connected, and classification layers [13]. In the last few years, deep learning has become a mainstay in the field of object detection [14] and classification and image segmentation.…”

Section: Introductionmentioning

confidence: 99%

Detection and Classification of Different Weapon Types Using Deep Learning

2021

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Today, with the increasing number of criminal activities, automatic control systems are becoming the primary need for security forces. In this study, a new model is proposed to detect seven different weapon types using the deep learning method. This model offers a new approach to weapon classification based on the VGGNet architecture. The model is taught how to recognize assault rifles, bazookas, grenades, hunting rifles, knives, pistols, and revolvers. The proposed model is developed using the Keras library on the TensorFlow base. A new model is used to determine the method required to train, create layers, implement the training process, save training in the computer environment, determine the success rate of the training, and test the trained model. In order to train the model network proposed in this study, a new dataset consisting of seven different weapon types is constructed. Using this dataset, the proposed model is compared with the VGG-16, ResNet-50, and ResNet-101 models to determine which provides the best classification results. As a result of the comparison, the proposed model’s success accuracy of 98.40% is shown to be higher than the VGG-16 model with 89.75% success accuracy, the ResNet-50 model with 93.70% success accuracy, and the ResNet-101 model with 83.33% success accuracy.

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Scene Recognition from Image Using Convolutional Neural Network

Cited by 10 publications

References 11 publications

Research and realization of computer image recognition system based on digital projection technology

Research and realization of computer image recognition system based on digital projection technology

Improving video surveillance systems in banks using deep learning techniques

Detection and Classification of Different Weapon Types Using Deep Learning

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