Lightweight CNN based on Spatial Features for a Vehicular Damage Detection System

Autonomous vehicles are a key element of the automotive industry, where the impact of the human factor on the condition of the vehicle and driving is minimized. An important element is the analysis of vehicular condition, which allows maintainence of its value and correct operation. We propose a system based on the analysis of the image of vehicles, which determines whether there is any damage. For this purpose, we propose a new model of a Convolutional Neural Network (CNN) that has 0. 395M trained values. The architecture of the network is adapted to the analysis of spatial features that allow networks to be adapted to analyze primarily vehicular shape and orientation in relation to other objects. The model also implements spatial dropout and regularization techniques for preventing overtraining and maintaining model generalization. The modeled architecture contributes to obtaining high classification accuracy at 94.78% using a public database and exceeding metrics of known transfer learning models.