Evolving Pre-Trained CNN Using Two-Layers Optimizer for Road Damage Detection From Drone Images

Abstract

There are numerous pre-trained Convolutional Neural Networks (CNN) introduced in the literature, such as AlexNet, VGG-19, and ResNet. These pre-trained CNN models could be reused and applied to tackle different image recognition problems. Unfortunately, these pre-trained CNN models are complex and have a large number of convolutional filters. To tackle such a complexity challenge, this research aims to evolve a pre-trained VGG-19 using an efficient two-layers optimizer. The proposed optimizer performs filters selection of the last layers of VGG-19 guided by the accuracy of the linear SVM classifier. The proposed approach has three main advantages. Firstly, it adopts a powerful two-layers optimizer that works with a micro swarm population. Secondly, it automatically evolves a lightweight deep model which uses a small number of VGG-19 convolutional filters. Thirdly, It applies the developed model for real-world road damage detection from drone-based images. To evaluate the effectiveness of the proposed approach, a total of 529 images were captured by using a drone-based camera for various road damages. Reported results indicated that the proposed model achieved 96.4% F1-score accuracy with a reduction of VGG-19 filter up to 52%. In addition, the proposed two-layers optimizer was able to outperform several related optimizers such as Arithmetic Optimization Algorithm (AOA), Wild Geese Algorithm (WGO), Particle Swarm Optimization (PSO), Comprehensive Learning Particle Swarm Optimization (CLPSO), and Reinforcement Learning-based Memetic Particle Swarm Optimization (RLMPSO).

Keywords

• Pre-trained CNN
• convolutional neural networks
• deep learning
• VGG-19
• road damage
• two-layers optimizer