Abstract
This paper studies the feature extraction and middle-level expression of Convolutional Neural Network (CNN) convolutional layer glass broken and cracked at the scene of road traffic accident. The image pyramid is constructed and used as the input of the CNN model, and the convolutional layer road traffic accident scene glass breakage and crack characteristics at each scale in the pyramid are extracted separately, and then the depth descriptors at different image scales are extracted. In order to improve the discriminative power of the depth descriptor, Hellinger kernel and Principal Component Analysis (PCA) are used to perform nonlinear and linear transformations. Two aggregation strategies based on depth descriptors are proposed to form a global image representation. The classification experiment of the data set shows that Hellinger kernel, PCA transformation, and two aggregation strategies are all conducive to improving the classification accuracy. In addition, the convolutional layer road traffic accident scene glass breaking and crack feature coding can obtain better classification performance than the fully connected layer feature. We conducted dynamic impact tests on plate glass and Polyvinyl Butyral- (PVB-) laminated glass under different boundary conditions and studied the crack propagation and failure process of the glass under different impact speeds. The results show that there are radial cracks and circular cracks on the glass specimens under the impact load; the glass specimens show partial damage under high-speed impact and the characteristics of glass breaking and cracks at the scene of road traffic accidents; the four-frame plate glass is supported by sharp dagger-like fragments. This paper compares the energy absorption of glass specimens under different boundary conditions. The results show that the energy absorption effect of the four-point supporting glass specimen is generally stronger than that of the four-frame supporting glass.
Highlights
Automobile glass is one of the important components of automobiles
E depth descriptors extracted from the Convolutional Neural Network (CNN) model are susceptible to the influence of the characteristic values of glass breakage and cracks at the scene of large road traffic accidents, which will affect the distance measurement between the descriptors. e Hellinger kernel transformation of the depth descriptor is a nonlinear transformation
After Hellinger kernel transformation, it can effectively weaken the influence of the characteristic value of glass breakage and crack on the scene of a large road traffic accident, and at the same time, it enhances the discrimination of the characteristic value of glass breakage and crack on the scene of a smaller road traffic accident. erefore, the Hellinger kernel transform makes the depth descriptor more sensitive to the characteristic value of glass breakage and cracks at the scene of minor road traffic accidents when measuring the distance, thereby enhancing the distinguishability of the depth descriptor
Summary
Automobile glass is one of the important components of automobiles. It integrates the functions of wind, rain, heat, and sound insulation and a safety component of the car. After the deep neural network is proposed, the technology for learning the characteristics of glass broken and cracks in road traffic accidents has made great progress. It can extract the characteristic expression of glass broken and cracks in road traffic accidents with high generalization ability and can improve the application of artificial intelligence in various applications. The deep Convolutional Neural Network (CNN) convolution layer is used to study the feature extraction and middle-level expression of glass broken cracks in local road traffic accidents. For four-point support plate glass, in addition to dagger-shaped fragments, scaly fragments will appear near the boundary
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