Applied Computer Vision through Artificial Intelligence

4Advancements in Computer Vision for Object Detection and Recognition using DenseNet Deep Learning Model

N. Deepa, Padmapriya L.*, Priyadarshini V. and Shree Harini S.

Department of Electronics and Communication Engineering, Velammal Engineering College, Chennai, India

Abstract

This paper described a novel automatic approach that combines hardware components with the DenseNet deep learning model. This work offers a new method for the autonomous identification of road surface deterioration. The road surface images are used to train the well-known, feature-efficient DenseNet model to detect various types of damage. The workflow includes real-time image acquisition from a camera or drone, which is then processed by the DenseNet model to determine their severity. The Arduino Uno activates the GSM module in response to detected damages, allowing for immediate alerts. System operation is intended to be continuous on a steady power supply. Communication ports enable efficient data transfer and potential system integration.

Keywords: Computer vision, deep learning, denseNet, crack detection, pavement damage, mask R-CNN, infrastructure monitoring, image processing

4.1 Introduction

In engineering, structures like beams and concrete surfaces often develop microscopic fractures due to cyclic loading and fatigue stress. Detecting these cracks is crucial for preventive actions [1]. Crack detection methods include destructive and non-destructive testing, with image-based techniques gaining ...