Deep-learning based trucks violation detection

Abstract

This project builds a truck detecting model for automatically supporting the traffic department Decide if the truck is overloaded or Normal-loaded to help the traffic department in controlling of high-way roads. We build the model based on convolutional neural network model. The dataset of the truck is constructed and hyper parameters modified of the convolutional neural network. A basic network model has successfully been trained by KERAS library. The model by KERAS library achieves 91.67% on overload / normal-overload truck classification, which isn't a bad result. we dived deeply in the model by changing the model to work by TENSORFLOW library. we optimized the model by TENSORFLOW library, the optimized TENSORFLOW model achieved 96% on the test set, that's better than that 91.67% of the KERAS model.