Pothole Detection System Using a Black-box Camera

Abstract

Aging roads and poor road-maintenance systems result a large number of potholes, whose numbers increase over time. Potholes jeopardize road safety and transportation efficiency. Moreover, they are often a contributing factor to car accidents. To address the problems associated with potholes, the locations and size of potholes must be determined quickly. Sophisticated road-maintenance strategies can be developed using a pothole database, which requires a specific pothole-detection system that can collect pothole information at low cost and over a wide area. However, pothole repair has long relied on manual detection efforts. Recent automatic detection systems, such as those based on vibrations or laser scanning, are insufficient to detect potholes correctly and inexpensively owing to the unstable detection of vibration-based methods and high costs of laser scanning-based methods. Thus, in this paper, we introduce a new pothole-detection system using a commercial black-box camera. The proposed system detects potholes over a wide area and at low cost. We have developed a novel pothole-detection algorithm specifically designed to work with the embedded computing environments of black-box cameras. Experimental results are presented with our proposed system, showing that potholes can be detected accurately in real-time.

Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images

“…To the best of our knowledge, no previous research covers such a wide variety of road damages, especially in the case of image processing. For example, the method proposed by Jo and Ryu () detects only potholes in D40, and that of Zalama et al. () classifies damage types exclusively as longitudinal and lateral, whereas the method proposed by Akarsu et al.…”

“…To the best of our knowledge, no previous research covers such a wide variety of road damages, especially in the case of image processing. For example, the method proposed by Jo and Ryu (2015) detects only potholes in (Maeda et al, 2016;Zhang et al, 2016) only detects the presence or absence of damage. Further, although the GAPs data set (Eisenbach et al, 2017) treats the crack as a crack, in our data set, the cracks are classified into five types.…”

Employing economical methods for pavement defects estimation

“…Another solution based on the video analysis was proposed by Jo and Ryu [11]. A system has been created which allows for detecting breakdowns of asphalt continuity with the use of an in-car camera recorder.…”

Um relato de experiência sobre a introdução da modelagem de processo de negócio para entendimento e validação de requisitos de um sistema de informação para gestão de vertedouro

“…O formulário da frente de concretagem, ilustrado na Figura 4 é dividido em cinco abas: Cadastrar (1), Tempo Trabalhado (2), Mão de Obra (3), Máquinas (4) e Croqui (5). Cada aba pode ser preenchida separadamente, sem que o usuário precise cadastrar as informações constantes das outras, bastando clicar sobre o botão Salvar Frente de Concretagem (6). Essas telas não são aqui apresentadas devido à necessidade de objetividade deste trabalho.…”

“…Há Sistemas específicos para acompanhamento de desgastes em pavimentos como [6] e [7]. Outro trabalho é o apresentado em [8], que visa o estudo do desgaste de concretos utilizados em usinas.…”